Mechanize

Mechanize

Junior Software Engineer

San Francisco · Junior

Sponsorship not specifiedDetected 368 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 refine RL tasks.
  • 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.
  • Want a product engineering role building features for end users
  • 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 $300,000 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

  • 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.

This listing is sourced directly from Mechanize's careers page and normalized into a canonical job model.