Mechanize

AI training environments for software engineering

Junior Software Engineer

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeSan Francisco, CA, USAIn-person

Earn $3.3k if you accept an offer

About the Job

Requirements

Strong technical fundamentals combined with an intuition for AI model behavior.
Able to code in Python.
Able to work independently.

Responsibilities

Design, build, and refine reinforcement learning tasks, where each task is a self-contained software engineering challenge with a prompt, an environment, and an automated grader; own the full lifecycle from idea generation to implementation, running and evaluating frontier models against the tasks, and iterating until the task is rigorous and fair.
Direct coding agents, evaluate their output, and determine when they are failing in subtle ways.

Desired Qualifications

Have just graduated or are about to graduate.
Can code in Python.
Are confident working independently.
Are motivated by problems that require both technical skill and creative cleverness.
No prior machine learning or artificial intelligence experience required.

Today, AI models learn coding from static datasets and toy problems. This team builds the realistic, simulated engineering environments — long-horizon tasks, real tooling, real failure modes — that frontier labs use to train and evaluate the next generation of coding agents.

What you'll work on

Design and build reinforcement-learning tasks and environments for software-engineering challenges
Own the full lifecycle: from task concept through evaluation against frontier models
Evaluate coding-agent outputs and identify subtle failure patterns
Build Python-based systems, harnesses, and tooling around model training and evals
Work with Docker/Linux environments that mirror real engineering work
Own problems end-to-end in a small, fast-moving team

Must-have

Strong Python skills
Experience working with the latest AI tools and models
Exceptional problem-solving ability — strong technical fundamentals and intuition for AI model behavior
Able to work independently
In person in San Francisco

Nice to have

ML or AI experience (e.g., you've trained your own models) — not required
Strong math/CS background (competitions like USACO/ICPC/olympiads)
Competed in quiz bowl, debate, robotics
Impressive personal projects

Why join

$300K base + equity + bonus — at the junior level
Your work directly shapes how frontier AI models learn to do real engineering
Small fast moving team, no bureaucracy — high ownership from day one
Work at the frontier of RL, agents, and evaluation with the top AI labs as your customers

We're hiring software engineers to build environments and evals for training and evaluating frontier coding agents. Learn more about our interview process and what working here is like. To get a better sense of the work we do, you can check out GBA Eval.

$300K salary · equity · bonus

About the Company

Mechanize

AI training environments for software engineering

Mechanize builds high-fidelity reinforcement learning (RL) environments used by leading AI labs to train models on real-world work tasks. Their simulated work scenarios mimic complex job functions–such as building features, debugging code, and deploying software–allowing AI systems to learn through performance and reward signals rather than static data. Mechanize doesn't train AI models itself or ship consumer products; instead, it focuses on creating the specialized virtual environments that help advance AI capabilities, with a current emphasis on automating software engineering and a long-term vision of enabling AI to handle valuable work across the economy. Backed by prominent tech investors, the company is actively hiring engineers to build these environments.

Company size: 11-50
Company stage: Seed
Total funding: Not listed
Headquarters: Not listed
Founded: Not listed

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