Machine Learning Engineer - Fully Remote | Upto $85/hr

mercor; San Francisco (CA); Hybrid; CAD 137,000 - 196,000; Part time

Job summary

Mercor is seeking an ML Engineer for a remote contract role focused on frontier AI coding agents. Responsibilities include tackling complex ML tasks, reviewing model-generated implementations, and evaluating training, inference, and MLOps outputs.

Ideal candidates have 2+ years of ML engineering experience, a track record building production ML systems, and familiarity with AI tooling such as Cursor, Claude Code, Codex, Windsurf, or Gemini CLI. You will work remotely on challenging AI projects.

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 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

Model Deployment

Code Review

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.