Computer Vision MLE (Train AI Models Part Time!)
hackajob · United States
- Hybrid
- Full-time
- $150,000 / year
- United States
Tailored resume — keyword-matched to this role.
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Job highlights
- Assess computer vision system feasibility for grading objects.
- Fine-tune modern vision foundation models.
- Benchmark model performance and measure accuracy.
- Evaluate data quality and production readiness.
- Report findings to a non-technical audience.
About the role
About the Role
hackajob is collaborating with Mercor to connect them with exceptional professionals for this role.
Mercor is placing a senior computer vision engineer on a delivery team for a focused 3-4 week technical assessment engagement, with a strong possibility of extending into a longer build. The work is a feasibility assessment of a computer vision system that identifies and grades physical objects from images. You will benchmark baseline model performance on a representative image sample, measure accuracy against a held-out set, assess data quality and the realistic performance ceiling, and translate the findings into a decision-grade report for a non-technical executive audience.
What we are looking for:
- 5+ years in computer vision and ML engineering, including hands-on fine-tuning of modern vision foundation models
- Experience classifying or grading physical objects from images (identification, condition or quality scoring, defect detection, or similar)
- Strong evaluation discipline: representative sampling, train/eval separation, honest accuracy benchmarking, and calibration
- Ability to assess the feasibility and production-readiness of a computer vision system and communicate the verdict clearly to a non-technical audience
Strong pluses:
- Experience with authentication, counterfeit, or anomaly detection
- Exposure to private equity diligence or other time-boxed advisory work
- Familiarity with imaging hardware and capture pipelines (cameras, lighting) and edge or on-prem deployment
Key skills/competency
- Computer Vision
- Machine Learning Engineering
- Model Fine-tuning
- Image Classification
- Object Grading
- Performance Benchmarking
- Accuracy Measurement
- Data Quality Assessment
- Feasibility Assessment
- Technical Reporting
Skills & topics
- Computer Vision
- Machine Learning
- ML Engineer
- AI
- Model Training
- Image Classification
- Object Detection
- Deep Learning
- Remote
- Technical Assessment
How to get hired
- Tailor your resume: Highlight your 5+ years in computer vision, ML engineering, and foundation model fine-tuning. Emphasize experience with object classification, grading, and evaluation discipline.
- Showcase relevant projects: Detail any experience with authentication, counterfeit, or anomaly detection, or private equity diligence in your application.
- Prepare for technical questions: Be ready to discuss your approach to benchmarking, accuracy measurement, and assessing production readiness of computer vision systems.
- Communicate effectively: Practice explaining complex technical concepts and findings clearly to a non-technical audience, as this is a key requirement for the role.
- Understand the engagement: Note the 3-4 week assessment period with potential for extension, and be prepared to discuss your availability and interest in such time-boxed advisory work.
Technical preparation
Practice fine-tuning modern vision foundation models.,Prepare to benchmark image classification accuracy.,Review data quality assessment techniques.,Rehearse explaining technical feasibility to executives.
Behavioral questions
Describe a challenging computer vision project.,How do you handle strict deadlines?,Explain technical findings to non-experts.,How do you assess feasibility of new tech?
Frequently asked questions
- What is the primary focus of this Computer Vision ML Engineer role at Mercor?
- The primary focus is a 3-4 week technical assessment to evaluate the feasibility of a computer vision system for identifying and grading physical objects from images. There's a possibility of extending into a longer build phase.
- What are the core responsibilities for a Computer Vision ML Engineer at Mercor?
- Core responsibilities include benchmarking model performance, measuring accuracy, assessing data quality and performance ceilings, and compiling a decision-grade report for executives. This involves hands-on fine-tuning of modern vision foundation models.
- What specific experience is Mercor seeking for this role?
- Mercor is looking for at least 5 years of experience in computer vision and ML engineering, with a strong emphasis on classifying or grading physical objects from images. A robust evaluation discipline is also crucial.
- Are there any preferred qualifications for this Computer Vision ML Engineer position?
- Strong pluses include experience with authentication, counterfeit, or anomaly detection, exposure to private equity diligence, and familiarity with imaging hardware and capture pipelines for edge or on-prem deployment.
- Is this Computer Vision ML Engineer position remote?
- Yes, the engagement is fully remote, allowing you to work from any location.
- How is compensation determined for this role?
- Compensation is set by Mercor's talent team, indicating a competitive package tailored to the role's requirements and your experience.
- What is the typical duration of this assessment engagement?
- The initial engagement is focused, typically lasting 3-4 weeks, designed as a technical assessment.
- What kind of reporting is expected from the Computer Vision ML Engineer?
- You are expected to translate your findings into a decision-grade report specifically for a non-technical executive audience, requiring clear and concise communication of technical results.