Senior ML Engineer Computer Vision
Micron Agritech
Job Overview
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Job Description
Senior ML Engineer Computer Vision
Micron Agritech is seeking a Senior ML Engineer specializing in Computer Vision to join our innovative team in Dublin, Ireland. This is a unique opportunity to own the end-to-end computer vision stack for AI-powered diagnostics in animal health. Your work will directly influence clinical decisions in live veterinary diagnostic environments across multiple countries. You will shape the architecture, set standards, and witness the real-world impact of your contributions. We are a well-funded, rapidly growing company at a pivotal stage, offering the chance to significantly influence how our technology scales.
What You Will Work On
Your responsibilities will include the detection, classification, and counting of biological targets within veterinary diagnostic imagery and video. This encompasses the entire model lifecycle: designing data pipelines, ensuring annotation quality, developing and optimizing models, and managing production deployment. You will operate without handoffs to a separate MLOps team and see your work implemented quickly. The technical challenges are substantial, involving noisy, variable, and demanding real-world veterinary imaging data. You will be tasked with building models that are robust, efficient, and trustworthy.
What We Are Looking For
- 5+ years of experience in Machine Learning, with at least 3 years focused on Computer Vision or Deep Learning.
- Proven experience shipping object detection models to production using frameworks like YOLO, DETR, or Detectron2.
- Strong Python engineering skills for data pipelines, automation, and tooling.
- Proficiency with PyTorch or TensorFlow.
- Solid experience with computer vision and video processing libraries such as OpenCV and ffmpeg.
- Strong SQL skills and experience with databases like Postgres.
- Comfort with Docker, Git, and CI/CD tools like GitHub Actions.
- Experience deploying on AWS, including EKS, Lambda, and S3.
- Familiarity with transformer-based CV architectures (e.g., DETR, ViT, Swin Transformer).
- A degree in Computer Science, Engineering, or a related field; a Master's degree is a plus.
Useful but not essential
- Experience with biological, microscopy, or diagnostic imaging.
- Knowledge of classical Computer Vision techniques.
- Familiarity with MLOps tooling such as MLflow, DVC, or W&B.
- Experience with frame-level video analysis or optical flow.
Why Join Us
- Equity for everyone: Receive shares and have a stake in the company's success.
- Competitive Salary: Earn compensation that reflects your value, with fair pay not substituted by mission passion.
- Real Ownership: As a senior hire, you will own your domain with direct access to the founding team.
- Meaningful Mission: Contribute to improving animal welfare and promoting a sustainable food system.
- Impactful Timing: Join at a crucial stage where your contributions will have a lasting visible impact.
Key skills/competency
- Senior ML Engineer
- Computer Vision
- Deep Learning
- Object Detection
- Python
- PyTorch
- TensorFlow
- AWS
- SQL
- Veterinary Diagnostics
How to Get Hired at Micron Agritech
- Tailor your resume: Highlight your 5+ years in ML, 3+ years in CV/Deep Learning, and production object detection experience (YOLO, DETR). Emphasize Python engineering, PyTorch/TensorFlow, and cloud (AWS) skills.
- Showcase relevant projects: Detail any experience with veterinary, microscopy, or diagnostic imaging, and classical CV techniques. Mention familiarity with MLOps tools if applicable.
- Prepare for technical interviews: Be ready to discuss your approach to building robust, efficient, and trustworthy models for noisy, variable data. Practice coding challenges in Python and SQL.
- Understand the mission: Articulate how your skills align with Micron Agritech's goal of improving animal welfare and food sustainability.
- Ask insightful questions: Inquire about the team's scaling plans, specific challenges in veterinary imaging, and opportunities for technical leadership.
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