Junior Software Engineer, Mechanize

$300k

+ Offers Equity

Python

Entry and Junior level

San Francisco Bay Area

5 days a week in office

Reinforcement learning environments for AI labs

Open for applications

Reinforcement learning environments for AI labs

1-20 employees

B2BArtificial IntelligenceMachine Learning

Open for applications

$300k

+ Offers Equity

Python

Entry and Junior level

San Francisco Bay Area

5 days a week in office

1-20 employees

B2BArtificial IntelligenceMachine Learning

Company mission

To develop virtual work environments, benchmarks, and training data that will enable the full automation of the economy.

Job

Company

Role

Who you are

Strong technical fundamentals combined with an intuition for AI model behavior
Are about to graduate or have 0-2 years of experience
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

What the job involves

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
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

Our take

Mechanize develops Reinforcement Learning environments that AI labs can use to train their models. The company believes that the greatest value of AI will be in automating ordinary tasks. However, current models lack the long-context capabilities and multimodality to execute long-term plans, which is what Mechanize's approach hopes to address.

Basically, the company supplies an environment in which AI models can be assigned tasks and graded on how well they complete them. These tasks can include things like building features, deploying apps or debugging issues. Then, the grade for the task asks as a reward signal to help the AI learn when it has done well vs badly.

In supplying these RL environments, Mechanize supports the more effective automation of everyday tasks, enabling businesses to extract real value from AI. The company is backed by investments from Nat Friedman and Daniel Gross, Patrick Collison, Dwarkesh Patel, Sholto Douglas and Marcus Abramovitch.

Kirsty

Company Specialist at Welcome to the Jungle

Company

Company HQ

SoMa, San Francisco, CA

Leadership

Matthew Barnett

(Co-Founder)

Formerly Staff Researcher at Epoch AI, Script Writer at Rational Animations and Data Scientist at Metaculus.

Tamay Besiroglu

(Co-Founder & CEO)

Previously co-founded Epoch AI, was a Research Scientist at MIT and Strategy and Operations Lead at Metaculus.

Ege Erdil

(Co-Founder)

Worked alongside Matthew and Tamay at Epoch AI.

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