GitHub Contributor

$50–150/hrrate as listed

micro1 is contracting senior software engineers to build reinforcement learning environments that test whether AI models can fix bugs, ship features, refactor code, and close performance gaps. Each task means producing a reproducible environment plus a golden reference solution, documented well enough that another engineer could verify it.

Summary, eligibility guidance and screening preparation are prepared by Sidequest from the platform's posting. Check the source listing before applying.

The work

You pick real software engineering problems — a defect in a diverse codebase, a missing feature, a refactor of legacy code, a performance bottleneck — and turn each one into a self-contained RL environment an AI model can be graded against. That means a reproducible setup (deterministic build, pinned dependencies, working test harness), a clearly scoped task statement, and a golden reference solution you can defend. You also document your technical reasoning: why this approach, what you ruled out, and how a grader distinguishes a correct fix from one that only passes tests by accident. Part of the workload is reviewing peer submissions for correctness and clarity.

The languages in scope are Python 3, Java, Rust, Go, C++, and TypeScript. Depth in one matters more than familiarity with all six. No AI or ML background is expected — the value you bring is engineering judgment about what makes a problem hard, what makes a solution genuinely correct, and where a plausible-looking patch quietly breaks something.

What the screen looks for

micro1's process is: screening questions, an AI-led interview of roughly 30 minutes, a tentative technical assessment, then hiring manager review. The interview probes for verifiable specifics rather than résumé summary — expect follow-ups that push on a bug you actually debugged, how you isolated it, and what the fix cost. A public GitHub or GitLab profile with real open source contributions is a hard requirement; be ready to walk through specific commits or PRs. Environment reproducibility is a recurring theme: they want people who instinctively think about flaky tests, hidden state, and version drift.

Logistics and pay

Contractor engagement, fully remote and asynchronous, around 15 hours per week
Compensation is output-based — paid per task that meets project spec, with weekly minimum submission requirements
Observed rates for this listing fall in the $50–150/hr range; because pay is per-task, effective hourly depends on your speed and how often work passes review, and nothing is guaranteed
Fast timeline: roles often fill within 48 hours, with first tasks expected 24–48 hours after onboarding

What they look for

Python3
JAVA
Rust
Algorithms
Basics C++
Typescript
bug fixing
feature implementation
codebase refactoring
performance optimization

Before you apply

Would you pass this screen?

Drawn from what this listing actually requires. Answered in your browser — nothing is sent to us, and we do not store your answers.

Do you have a public GitHub or GitLab profile with code contributions you can share that demonstrates production-level work in at least one of these languages: Python, Java, Rust, Go, C++, or TypeScript?
Public open source contributions · required
Do you have at least 5 years of professional software engineering experience shipping production code?
Senior software engineering experience · required
Can you commit to approximately 15 hours per week and start new tasks within 24–48 hours of completing onboarding?
Weekly availability · required
Are you proficient in at least one of the following languages for production work: Python3, Java, Rust, Go, C++, or TypeScript?
Language fluency · required
Are you able to work remotely and have a reliable internet connection?
Remote work · preferred

0 of 5 answered

Included with this listing

Interview prep — 10 questions

Matched to what this platform's screening actually measures. Answer each out loud in 60–90 seconds: claim → concrete example → implication.

What it tests: Whether you have genuine experience shipping real code to production or a live codebase, not toy projects or coursework. The listing requires 'clear open source contributions' you can showcase.
A strong answer: Name a specific project, describe a concrete technical problem it addressed, and explain your measurable contribution (e.g., 'I implemented the parser rewrite that reduced memory overhead by 30%' or 'I led the refactor of the authentication module across 5,000 lines'). Ideally mention merge/release details.
Where people lose it: Claiming you 'contributed to' a large project but being vague about what you actually wrote or reviewed. Or naming a project and then describing work that sounds like a tutorial or sandbox, not something in active use by others.