You'll connect one of your own private codebases and turn a real engineering problem from it into a task: a clear, maintainer-style issue, a hidden test suite, and a reference solution. Every submission is validated automatically before it's approved, so the ideal candidate is someone who can write a problem a strong engineer would recognize as real work, and a test suite that verifies a solution the way a careful reviewer would.
Why Apply
Fully remote and flexible — work on your own schedule, no fixed hours.
Get paid per codebase and per approved task — $300 per codebase, $75 per fully approved task, with no cap on how many you submit.
Responsibilities
Connect a substantial private codebase of your own to author tasks against.
Author an original coding task from that codebase: a clear, maintainer-style issue describing the problem to solve.
Build a hidden test suite for each task, including both fail-to-pass tests (fail before the fix, pass after) and pass-to-pass tests (verify nothing else breaks).
Write a reference solution that resolves the issue and passes every test in the hidden suite.
Iterate on submissions as needed — you'll have up to 5 attempts to get a task through automated validation, so take your time rather than rushing it.
Required skills
Proficiency in at least one modern programming language (e.g., JavaScript/TypeScript, Java, Go, Rust, C/C++, Python, etc.).
Access to a substantial, complex, private codebase you own and can author tasks against.
Strong technical writing, documentation, and testing skills.
Ability to reason about how a bug or feature ripples across a multi-file codebase.
Preferred skills
University students with software engineering internship experience.
Students majoring in Computer Science, Data Science, or related fields.
Open source contributors with a track record of real commits/PRs.
Background in code review or test authoring.
Eligibility
Work arrangement: Fully remote
Compensation details
$150 per hour.
$300 per codebase you connect and use as a basis for tasks.
Thriveth makes AI data-training work easier to find, understand, and navigate. We replace uncertainty with clear opportunities, realistic expectations, and insights from real application journeys.