Mechanize
Software Engineer
San Francisco
Sponsorship not specifiedDetected 154 days ago
PythonMachine LearningMentoring
About the role
- Learn more at mechanize.work http://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.
- Each task is a self-contained software engineering challenge with a prompt, an environment, and an automated grader.
Responsibilities
- You'll design, build, and quality-assure RL tasks.
- You own the full lifecycle: ideation, grading infrastructure, running frontier models against the task, failure analysis, and iteration.
- You may also contribute to shared infrastructure: improving our build pipeline, automating parts of QA, or building tooling for other engineers.
- 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 check-ins and feedback.
- You own your tasks from start to finish, with regular check-ins and feedback.
Requirements
- No prior ML or AI experience required
Compensation
- Compensation includes a $350,000 base salary, equity, and performance bonuses. Top performers can earn more in bonuses than in base salary.
- 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 https://www.nytimes.com/2025/06/11/technology/ai-mechanize-jobs.html, the Dwarkesh Podcast https://www.dwarkesh.com/p/ege-tamay and Hard Fork https://www.youtube.com/watch?v=M5Lycj5IRwQ.
- Learn more about the 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
Benefits
- include health, dental, vision, and life insurance.
Company info
- At this level, we expect you to consistently produce tasks that target meaningful capability gaps in frontier models, and to develop a strong sense for what makes a task informative versus merely difficult.
- 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.
- At this level, we expect extensive familiarity with what frontier coding agents can and can't do.
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This listing is sourced directly from Mechanize's careers page and normalized into a canonical job model.