ML Engineer - AI Coding Expert

mercor; Deutschland; Remote; EUR 74,000 - 135,000; Part time

Benefits offered by this job

Remote contract work

Job summary

Mercor is seeking an ML Engineer (Coding Agent Experience) on a contract basis. The role focuses on leveraging frontier AI coding agents to complete and evaluate complex ML engineering tasks, review model-generated implementations, and identify performance issues. Remote work is offered, with compensation tied to accepted tasks.

Qualifications

• 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.

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.

Skills

ML engineering

Production ML systems

AI coding agents usage

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

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.

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.