Design, build, and refine reinforcement learning tasks, tackling complex system design problems and mentoring junior team members.
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, owning the full lifecycle from ideation through grading, failure analysis, and iteration. At this level, we expect you to work on our most complex tasks: environments involving multi-step workflows, realistic stakeholder interactions, large codebases with real conventions and technical debt, or challenging system design problems.
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. You will also contribute to shared infrastructure and tooling, and may take on mentorship responsibilities for newer team members.
What makes someone good at this
Deep software engineering experience across multiple domains, combined with a strong intuition for AI model behavior. You need to anticipate where a model will take shortcuts, distinguish genuine capability gaps from grader issues, and design tasks that target deeper, more subtle failure modes from areas you know well: infrastructure, distributed systems, performance, security, or other specializations.
Good fit if you:
Have deep expertise in at least one area of software engineering
Can code in Python
Are confident working independently on complex, ambiguous problems
Have extensive experience working with coding agents
No prior ML or AI experience required
Probably not a good fit if you:
Want a product engineering role building features for end users
This is independent, high-ownership work. You own your tasks from start to finish, with regular feedback. Strong performers are recognized and rewarded. Benefits include 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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