Frontier AI Data Engineer — Model Evaluation & ETL

Mercor; San Francisco (CA); On-site; USD 207,000 - 234,000; Part time

Job summary

Mercor is partnering with a leading AI research lab to support a Frontier Code Agents project. You will help evaluate frontier AI coding models through structured technical assessments and focus on realistic data engineering workflows.

The role involves reviewing model-generated ETL pipelines, data warehouses, analytics platforms, and distributed data systems, and applying engineering judgment to identify bugs and scalability issues. Sprint-based work with rapid task turnover is expected.

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

Analytics platforms

Distributed data systems

AI coding agents

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. You will help evaluate frontier AI coding models through structured technical assessments and focus on realistic data engineering workflows.

The role involves reviewing model-generated ETL pipelines, data warehouses, analytics platforms, and distributed data systems, and applying engineering judgment to identify bugs and scalability issues. Sprint-based work with rapid task turnover is expected.