ML Engineer for Frontier AI Code Agents

Mercor; Toronto; Remote; CAD 248,000 - 358,000; Full 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.

The work focuses on realistic machine learning engineering workflows and model evaluation. This sprint-based engagement seeks engineers with ML experience to review model-generated implementations, identify bugs, compare frontier models, and apply professional engineering judgment to realistic ML

Qualifications

• 2+ years of professional ML 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.

Responsibilities

• Use frontier AI coding agents to complete and evaluate complex ML and AI engineering tasks.
• Review model-generated implementations involving training, inference systems, MLOps, and LLM applications.
• 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

MLOps

LLM applications

AI coding agents

Cursor

Claude Code

Codex

Windsurf

Gemini CLI

Tools

Cursor

Claude Code

Codex

Windsurf

Gemini CLI

Job description

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. This sprint-based engagement seeks engineers with ML experience to review model-generated implementations, identify bugs, compare frontier models, and apply professional engineering judgment to realistic ML