About the position We are sharing a specialised part-time consulting opportunity for senior computer vision and machine learning professionals experienced in vision foundation models, object identification, image-based quality scoring, defect detection, model benchmarking, feasibility assessment, and executive-level technical reporting. This role supports current and upcoming remote consulting opportunities focused on computer vision feasibility assessment, image model evaluation, baseline benchmarking, data quality review, performance ceiling analysis, production-readiness assessment, and high-quality project execution. Selected professionals will evaluate whether a computer vision system can reliably identify and grade physical objects from images and translate findings into a clear decision-grade report.

Responsibilities

• Assess the feasibility of a computer vision system designed to identify, classify, or grade physical objects from images
• Evaluate whether model performance is strong enough for practical use based on available data, task complexity, and expected accuracy standards
• Assess data quality, image quality, label quality, class balance, edge cases, and realistic performance ceilings
• Identify technical gaps that may affect reliability, scalability, or production readiness
• Benchmark baseline model performance on a representative image sample
• Measure accuracy against a held-out evaluation set using appropriate metrics and validation practices
• Apply strong evaluation discipline, including representative sampling, train/eval separation, honest benchmarking, and calibration
• Review model performance across tasks such as object identification, condition scoring, quality grading, defect detection, anomaly detection, or similar image-based classification tasks
• Translate technical findings into a clear, decision-grade report for a non-technical executive audience
• Explain feasibility, expected limitations, data constraints, model performance, and recommended next steps
• Document methodology, assumptions, evaluation results, and technical conclusions clearly
• Provide practical guidance on whether the system should proceed, be refined, or require additional data and testing

Requirements

• 5+ years of experience in computer vision, machine learning engineering, applied ML, or related technical work
• Hands-on experience fine-tuning modern vision foundation models
• Experience classifying or grading physical objects from images, including identification, condition scoring, quality scoring, defect detection, or similar use cases
• Strong understanding of evaluation design, representative sampling, train/eval separation, accuracy benchmarking, calibration, and validation methodology
• Ability to assess feasibility and production readiness of a computer vision system
• Strong written communication skills and ability to explain technical findings clearly to non-technical stakeholders
• Ability to work independently in a remote, project-based environment
• Academic backgrounds in computer science, machine learning, artificial intelligence, data science, electrical engineering, robotics, applied mathematics, statistics, or related fields may be highly relevant
• Professional experience in computer vision, ML engineering, applied research, model evaluation, image analysis, or technical assessment may be especially valuable
• Equivalent hands-on computer vision and ML experience may be considered depending on project needs

Nice-to-haves

• Experience with authentication, counterfeit detection, anomaly detection, defect detection, or quality inspection
• Exposure to private equity diligence, technical due diligence, feasibility assessments, or other time-boxed advisory work
• Familiarity with imaging hardware and capture pipelines, including cameras, lighting, controlled image capture, or dataset collection
• Experience with edge deployment, on-prem deployment, production ML systems, or applied computer vision pipelines
• Ability to produce clear technical recommendations under a focused project timeline

Benefits

• Competitive hourly compensation
• Remote structure
• Flexible scheduling
• Weekly payments via Stripe or Wise