Frontier AI Data Engineer for Model Evaluation

Obsidian; Toronto; Remote; CAD 227,000 - 324,000; Part time

Job summary

Obsidian is seeking contributors to advance frontier AI coding models through structured technical assessments. You will engage with frontier AI coding agents to perform complex data engineering tasks and evaluate model-generated data infra, pipelines, and analytics platforms.

The role emphasizes identifying edge cases, scalability issues, and comparing model outputs across multiple tools, applying professional judgment to realistic data engineering scenarios.

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.

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

AI coding agents

Distributed data systems

Tools

Cursor

Claude Code

Codex

Windsurf

Gemini CLI

Job description

Obsidian is seeking contributors to advance frontier AI coding models through structured technical assessments. You will engage with frontier AI coding agents to perform complex data engineering tasks and evaluate model-generated data infra, pipelines, and analytics platforms.

The role emphasizes identifying edge cases, scalability issues, and comparing model outputs across multiple tools, applying professional judgment to realistic data engineering scenarios.