Data Engineer - AI Model Evaluation

Mercor; Greater London; On-site; GBP 122,000 - 408,000; 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 data engineering workflows and model evaluation, with sprint-based delivery. Spots are limited and filling quickly on a first come, first serve basis, offering short, clearly scoped tasks and prompt payouts. Experience with ETL pipelines and data platforms is valued.

Qualifications

• 2+ years of professional data engineering experience.
• Experience building ETL pipelines, data warehouses, analytics platforms, or distributed data systems.
• Proficient with AI coding agents and evaluating model outputs.
• Experience operating large-scale data platforms 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 exp

ETL pipelines

Distributed data systems

AI coding agents

Data platform ops

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.