Machine Learning Engineer for AI Model Evaluation

This position involves collaborating with a prominent AI research lab to enhance frontier AI coding models through structured technical assessments. Contributors will engage in realistic machine learning engineering workflows, focusing on model evaluation and improvement.

• Utilize frontier AI coding agents to execute and assess complex machine learning and AI engineering tasks.
• Review model-generated implementations, including model training, inference systems, MLOps, and LLM applications.
• Identify bugs, edge cases, performance issues, and failure modes in AI models.
• Compare outputs from various frontier models, assessing their strengths and weaknesses.
• Apply professional engineering judgment to realistic machine learning engineering scenarios.

This is a sprint-based project requiring a time commitment of 12-24 hours based on client needs.

Compensation is set at $400 per accepted task, with typical tasks taking approximately 2-3 hours after an initial ramp-up period.

• Minimum of 2 years of professional experience in machine learning engineering.
• Experience in building production ML systems, model deployment infrastructure, LLM applications, or AI-powered products.
• Regular use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or similar tools.
• Ability to evaluate model-generated machine learning implementations and assess technical tradeoffs.
• Experience deploying ML systems to production is preferred.