We're building a dataset to evaluate AI coding agents - how well a model handles real-world developer tasks. You'll create challenging tasks and evaluation criteria within realistic simulated environments:

• Build realistic developer environments - a virtual company with codebase, infrastructure, and context (tickets, docs, conversations) that forms a believable development history
• Design tasks from intermediate states of these environments - craft the prompt, define what "solved" means, and ensure the task is solvable by an AI agent
• Write tests that verify agent solutions - accept all valid approaches and reject incorrect ones, neither too strict nor too lenient
• Iterate on tasks and tests based on QA feedback - review agent solutions, analyze failures, and refine until the evaluation is fair and robust

What this is NOT:

• Not data labeling
• Not prompt engineering
• Not writing code from scratch - the agent writes most of the code; you guide and evaluate

What we look for:

• 8+ years in software development
• Core stack: Python (FastAPI), JavaScript/TypeScript (React), Docker, Postgres, Kafka, Redis
• Experience writing tests (functional, integration)
• English proficiency - B2+

Why this is hard:

• Frontier models are already good at coding. Creating a task that genuinely challenges the best models is non-trivial. You need to deeply understand where models fail and what scenarios reveal the difference between a good and a bad solution.
• Tasks have many valid solutions - writing tests that accept all correct solutions and reject incorrect ones is harder than it sounds.

Desired Candidate Profile

• 8+ years in software development
• Core stack: Python (FastAPI), JavaScript/TypeScript (React), Docker, Postgres, Kafka, Redis
• Experience writing tests (functional, integration)
• English proficiency - B2+