Design, build, and refine RL tasks to improve AI models by identifying and evaluating failures in complex coding challenges.
About Mechanize
Mechanize builds reinforcement learning environments that frontier AI labs use to train and evaluate their coding models. Learn more at mechanize.work.
Why the work matters
AI models have gotten good at narrow coding tasks but still fail at the complex, judgment-heavy parts of software engineering. We build the environments that expose those failures and help models improve.
What you'll do
You'll design, build, and refine RL tasks. Each task is a self-contained software engineering challenge with a prompt, an environment, and an automated grader. You own the full lifecycle: coming up with the idea, implementing the grading infrastructure, running frontier models against the task, analyzing where and why they fail, and iterating until the task is rigorous and fair.
Coming up with good task ideas requires being clever: finding situations where a frontier model will fail in interesting ways, which means seeing gaps that the model itself doesn't see. You will use coding agents heavily, and a large part of the job is directing them well, evaluating their output, and knowing when they are failing in subtle ways.
What makes someone good at this
Strong technical fundamentals combined with an intuition for AI model behavior. You need to anticipate where a model will take shortcuts, distinguish genuine capability gaps from grader issues, and understand how a model will interpret a prompt. Most engineers significantly underestimate what frontier coding agents can already do; candidates who have spent significant time working with them will have a real head start.
Good fit if you:
Are graduating in 2026 or 2027
Can code in Python
Are confident working independently
Are motivated by problems that require both technical skill and creative cleverness
No prior ML or AI experience required
Probably not a good fit if you:
Want a product engineering role building features for end users
Prefer a highly collaborative team environment with shared ownership
Want extensive structured mentorship
This is independent, high-ownership work. You own your tasks from start to finish, with regular check-ins and feedback. Strong performers are recognized and promoted quickly. Benefits include 401k, health, dental, vision, and life insurance. Applying takes less than one minute.
Interview process: https://www.mechanize.work/how-our-interview-process-works
Learn more about the work: https://www.mechanize.work/what-working-here-is-like
About Mechanize. ~20 person team in San Francisco. Backed by Patrick Collison, Nat Friedman, Daniel Gross, Jeff Dean, Dwarkesh Patel, and Sholto Douglas. Featured in the New York Times, the Dwarkesh Podcast and Hard Fork.
Mechanize specializes in creating reinforcement learning environments designed to train AI models for real-world tasks. Our products cater to top AI labs, providing them the tools necessary to push the boundaries of full economic automation.
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