Computer Vision MLE (Train AI Models Part Time!)
hackajob · United States
- Hybrid
- Full-time
- $150,000 / year
- United States
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Computer Vision MLE (Train AI Models Part Time!)
hackajob · United States
Casey Reed
Hiring Manager · h•••••@hackajob.com
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Subject: Interested in the Computer Vision MLE (Train AI Models Part Time!) role at hackajob
Hi Casey — I came across the Computer Vision MLE (Train AI Models Part Time!) opening and wanted to reach out directly. I've spent the last few years doing exactly this kind of work, and hackajob stood out because…
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Not recommended alone — most applicants never hear back.
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?
Prefer to apply the usual way?
Not recommended alone — most applicants never hear back. Email the hiring manager first.
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.
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