Data Engineer - AI Model Evaluator

Obsidian; Toronto; On-site; CAD 413,000 - 689,000; Full time

Job summary

Mercor partners with a leading AI research lab to advance frontier code agents through structured technical assessments of data engineering workflows and model evaluations.

You will review complex ETL pipelines, data warehouses, analytics platforms, and distributed data systems while identifying bugs and scalability issues using frontier AI coding agents.

Qualifications

• 2+ years of professional data engineering experience.
• Experience building ETL pipelines, data warehouses, analytics platforms, or distributed data systems.
• Regular use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or similar tools.
• Ability to evaluate model-generated data infrastructure and pipeline implementations.
• Experience operating large-scale data platforms is preferred.

Responsibilities

• Use frontier AI coding agents to complete and evaluate complex data engineering tasks.
• Review model-generated implementations involving ETL pipelines, data warehouses, analytics platforms, and distributed data systems.
• Identify bugs, edge cases, scalability issues, and failure modes.
• Compare outputs from multiple frontier models and assess their strengths and weaknesses.
• Apply professional engineering judgment to realistic data engineering scenarios.

Skills

Data engineering

ETL pipelines

Data warehouses

Distributed data systems

AI tooling

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 data 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 data engineering tasks.
• Review model-generated implementations involving ETL pipelines, data warehouses, analytics platforms, and distributed data systems.
• Identify bugs, edge cases, scalability issues, and failure modes.
• Compare outputs from multiple frontier models and assess their strengths and weaknesses.
• Apply professional engineering judgment to realistic data 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 data engineering experience.
• Experience building ETL pipelines, data warehouses, analytics platforms, or distributed data systems.
• Regular use of AI coding agents such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or similar tools.
• Ability to evaluate model-generated data infrastructure and pipeline implementations.
• Experience operating large-scale data platforms is preferred.