AI/ML Engineer - Fully Remote | Upto $85/hr

mercor; San Francisco (CA); Hybrid; GBP 75,000 - 102,000; Full time

Job summary

Mercor seeks an ML Engineer (Coding Agent Experience) to work remotely on a contract basis. You will leverage frontier AI coding agents to tackle complex ML tasks and evaluate model-generated implementations. The role emphasizes robust deployment considerations and rigorous analysis of ML systems.

You will review model training, inference, and MLOps workflows while identifying bugs, edge cases, and performance issues. Strong judgment is essential for real-world ML engineering scenarios.

Qualifications

• 2+ years of professional ML engineering experience.
• Experience building production ML systems and deployment infrastructure.
• Regular use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or similar tools.
• Ability to evaluate model-generated ML implementations and technical tradeoffs.

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 in AI models.
• Compare outputs from multiple frontier models and assess their strengths and weaknesses.
• Apply professional engineering judgment to realistic ML engineering scenarios.

Skills

ML engineering

Production ML

AI coding agents

Model evaluation

Tools

Cursor

Claude Code

Codex

Windsurf

Gemini CLI

Job description

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position: ML Engineer (Coding Agent Experience)
Type: Contract
Compensation: $85/hour
Location: Remote

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 in AI models.
• Compare outputs from multiple frontier models and assess their strengths and weaknesses.
• Apply professional engineering judgment to realistic ML engineering scenarios.

Qualifications

Must-Have

• 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.

Preferred

• Experience deploying ML systems to production.

Compensation & Legal

• $400 per accepted task
• Compensation is tied to accepted work.