ML Engineer - AI Coding Expert

Mercor; Mumbai; On-site; 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, with sprint-based tasks and compensation per accepted task.

Qualifications

• 2+ years of professional ML engineering experience.
• Experience with production ML systems and deployment.
• Experience with AI coding agents and evaluation of model outputs.

Responsibilities

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

Skills

Machine learning engineering

Production ML systems

MLOps

LLM applications

Code evaluation

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.