ML Engineer: Frontier AI Coding Evaluator

Mercor; Miami (FL); On-site; USD 455,000 - 647,000; 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.

The work focuses on realistic machine learning engineering workflows and model evaluation. As a contractor you will use frontier AI coding agents to complete and evaluate complex ML engineering tasks, review model-generated implementations, identify bugs and performance issues, and compare outputs

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.

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

MLOps

LLM applications

AI coding agents

Model deployment

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. As a contractor you will use frontier AI coding agents to complete and evaluate complex ML engineering tasks, review model-generated implementations, identify bugs and performance issues, and compare outputs