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cancelMachine Learning EngineerSAIC • edge-to-cloud MLAshburn, Virginia, United States9 days ago
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In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Machine Learning EngineerSAIC • Edge AI focusAshburn, Virginia, United States9 days ago
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
DevOps EngineerLightedge • Fast-pacedOmaha, Nebraska, United States9 days ago
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Senior Software Engineer AI Telephony SystemsRed Ventures • AI Telephony Leader NYCCharlotte, North Carolina, United States9 days ago
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Media Strategy ManagerMeta • Global brand leadershipMenlo Park, California, United States9 days ago
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Technical Project ManagerNTT DATA, Europe & LATAM, Branch in USA, Inc. • Large tech services firmBoston, Massachusetts, United States8 days ago
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Apply
Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Principal Product Manager - Cloud Storage PerformanceNetApp, Inc. • Industry leaderDurham, North Carolina, United States9 days ago
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Personalized Resume
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Senior Language Engineer AGI Data ServicesAmazon • Multimodal datasetsBellevue, Washington, United States9 days ago
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Personalized Resume
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Manager, Account ManagementMastercard • Top-tier global financeNew York, New York, United States1 month ago
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Personalized Resume
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Account Executive, Commercial AcquisitionSnowflake • High growth, commission, equityNew York, NY, United States1 month ago
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
A&R AssistantSony Music Entertainment • Global music leaderNew York, NY, United States1 month ago
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Assistant Project ManagerMace • Global financial projectsNew York, United States1 month ago
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Executive Assistant, Office of the CEOFalconX • Hybrid role NYCNew York, New York, United States1 month ago
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Account Executive, SMBMimecast • Lexington, MA hybridMassachusetts, United States1 month ago
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Personalized Resume
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Senior Manager, Strategic Account ManagementWalmart • Walmart, New York, strategic roleNew York, New York, United States1 month ago
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Mid-Market Account ExecutiveGong • High growth tech leaderNew York, New York, United States1 month ago
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Personalized Resume
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Manager, Technical AccountingMastercard • High-impact leadership rolePurchase, New York, United States1 month ago
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Personalized Resume
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Internal Audit Analyst - Capital PlanningGoldman Sachs • Top global investment bankNew York, New York, United States1 month ago
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Personalized Resume
Apply
Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Audit Manager II - Financial CrimesTD • Top global bankNew York, New York, United States1 month ago
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Personalized Resume
Apply
Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Staff AccountantRapid Response Monitoring • In-office in SyracuseSyracuse, New York, United States1 month ago
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Email Hiring Manager
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In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Manager, Mergers and AcquisitionsMastercard • Competitive salary, global impactPurchase, New York, United States1 month ago
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Personalized Resume
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Corporate M&A Mid-Level AssociateFenwick & West • Top M&A firmNew York, United States1 month ago
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Personalized Resume
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Senior Medical Administrative SecretaryUMass Memorial Health • Multi-site administrative roleWorcester, Massachusetts, United States1 month ago
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Systems Engineer Autonomous Maritime PlatformsGeneral Dynamics Mission Systems • Quincy MA facilityQuincy, Massachusetts, United States1 month ago
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
Special Agent STEM Engineering BackgroundFederal Bureau of Investigation (FBI) • Federal agency roleNew York, United States1 month ago
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Personalized Resume
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Email Hiring Manager
Interview
In a nutshell
check
Leads ML model development for vision and biometrics.check
Deploys models to edge GPUs with optimization.check
Works with GCP Vertex AI and MLOps pipelines.check
Integrates multi-sensor data for robust performance.check
Requires 5 years of ML experience and Python proficiency.Full Description
Machine Learning Engineer
SAIC is seeking an experienced Machine Learning Engineer with strong expertise in computer vision, biometrics, and image processing to support the development and deployment of advanced AI/ML capabilities across our surveillance and sensor-integration platforms. This role focuses on designing, optimizing, and operationalizing ML models that run on both edge GPU systems and our cloud training environment.
Key Responsibilities
- Computer Vision & Biometrics Model Development: Develop, train, and optimize deep learning models for object detection, tracking, classification, OCR/LPR, face recognition, and other biometric modalities; improve robustness under challenging conditions.
- Edge AI Inference & Optimization: Deploy ML models onto GPU-accelerated edge compute devices; optimize with TensorRT, ONNX Runtime; manage latency and throughput for real-time workloads.
- Cloud Integration & MLOps (Highly Desirable): Collaborate on training pipelines, data ingestion/labeling, model evaluation and versioning; work with Vertex AI Pipelines, Vertex AI Training/Endpoints, Cloud Storage, and BigQuery; contribute to CI/CD for ML and artifact tracking.
- Data Engineering & Sensor Fusion: Build datasets from EO/IR cameras, LPR/QR/RFID sources, and radar-aligned video; implement multi-modal correlation; perform iterative retraining.
- Testing & Field Validation: Support lab and field testing; evaluate accuracy, latency, throughput, and precision/recall; document findings and proposals for deployment.
- Collaboration & Documentation: Work with architects, software/hardware engineers, and delivery teams; participate in design/code reviews; produce clear technical docs on model behavior and data constraints.
Required Qualifications
- Bachelor’s in Computer Science, Electrical Engineering, Machine Learning, or related discipline; 5 years of experience in ML model design/implementation (additional years may substitute for degree).
- Hands-on experience with PyTorch or TensorFlow; computer vision algorithms and data augmentation; object detection (YOLO, SSD, Faster R-CNN, etc.); OCR/LPR workflows; face detection/recognition or other biometrics.
- Practical experience deploying/optimizing models for real-time GPU inference; strong Python; familiarity with C++ is a plus; Docker containers and modern dev workflows.
Preferred / Highly Desirable Qualifications
- Experience with GCP Vertex AI Training, Pipelines, and Endpoints; Vertex AI Model Registry; in-depth MLOps experience (CI/CD for ML, model versioning, monitoring).
- Background with multi-sensor systems (EO/IR, radar, RFID, OCR/LPR integration); edge-optimized ML; familiarity with Kafka or distributed streaming.
- Experience on government/defense or mission-critical systems; strong analytical thinking and scientific rigor; ability to work independently with cross-functional teams; excellent communication.
What We Offer
Opportunities to contribute to rugged, edge-to-cloud AI capabilities, exposure to large-scale sensor fusion, and impact across national security solutions. Competitive compensation and benefits package, with career growth opportunities in ML engineering and systems engineering.
Key skills/competency
- computer vision
- biometrics
- edge inference
- gpu optimization
- yolo
- ocr
- lpr
- vertex ai
- mlops
- sensor fusion
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