ML Engineer

Role Responsibilities

• Use frontier AI coding agents to complete and evaluate complex machine learning and AI engineering tasks
• Review model-generated implementations involving model training, inference systems, MLOps, and LLM applications
• Identify bugs, edge cases, performance issues, and failure modes
• Compare outputs from multiple frontier models and assess their strengths and weaknesses
• Apply professional engineering judgment to realistic ML engineering scenarios

Time Commitment

Sprint based project that runs in 12-24 hour stretches based on client requirement

Candidature

• 2+ years of professional machine learning engineering experience
• Experience 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
• Experience evaluating model-generated machine learning implementations and technical tradeoffs
• Experience deploying ML systems to production is preferred

Compensation

$400 per accepted task

Typical tasks take approximately 2-3 hours after ramp-up

Compensation is tied to accepted work