ML Engineer - AI Coding Expert

Obsidian; Toronto; On-site; CAD 767,000 - 863,000; Part time

Job summary

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.

You will review model-generated implementations across training, inference systems, MLOps, and LLM applications, applying professional engineering judgment to realistic 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, or Gemini CLI.
• Ability to evaluate model-generated ML implementations and tradeoffs.
• Experience deploying ML systems to production is preferred.

Responsibilities

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

Skills

ML engineering

Production ML systems

Model deployment

LLM applications

AI-powered products

Evaluation of ML implementations

Tools

Cursor

Claude Code

Codex

Windsurf

Gemini CLI

Job description

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.