ML Engineer (Coding Agent Experience)

Engineering & Software • Independent Contractor • Remote • Weekly Payouts

About the Role

• Mercor is partnering with a leading AI research lab to support a Frontier Code Agents project.

• Contributors help evaluate and improve frontier AI coding models through structured technical assessments.

• The work focuses on realistic machine learning engineering workflows and model evaluation.

• Spots are limited and filling quickly on a first come, first serve basis.

What You'll Do

• 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.

Compensation

• $400 per accepted task.

• Typical tasks take approximately 2, 3 hours after ramp-up.

• Compensation is tied to accepted work.

Who Should Apply

• 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.

• Ability to evaluate model-generated machine learning implementations and technical tradeoffs.

• Experience deploying ML systems to production is preferred.

Details

• Pay: $85 per hour (hourly)
• Commitment: 40 hours per week • remote
• Eligible locations: United States
• Platform: Mercor (weekly payouts via Stripe or Wise)

Submit your application via the link below. Qualified candidates move through Mercor's short AI interview and selection process. Projects can be extended, shortened, or concluded early depending on needs and performance.