We are hiring experienced software engineers to design long-horizon coding tasks: realistic, investigation-heavy engineering problems drawn from substantial codebases, used to evaluate and train advanced AI coding agents.
The ideal candidate is comfortable working across large multi-file codebases, reasoning about how a bug or feature ripples across modules, and building reproducible, containerized environments backed by rigorous tests. You should be able to author a problem a strong engineer would recognize as real work, and a verifier that grades a solution the way a careful reviewer would. Experience with Docker and containerized workflows is required. You will also need a substantial private codebase of your own to author tasks against.
Responsibilities
Author long-horizon coding tasks grounded in real engineering problems (bugs, features, regressions, migrations) from substantial codebases.
Write clear prompts that require genuine investigation and complicated execution by a coding agent.
Build deterministic test suites and review criteria that verify correctness, catch regressions, and resist shortcut or "reward-hacking" solutions.
Containerize tasks with Docker so they build and run reproducibly.
Required skills
Proficiency in at least one modern programming language (e.g., JavaScript/TypeScript, Java, Go, Rust, C/C++, Python, etc.).
Hands-on experience with Docker or similar containerization tools.
Strong technical writing, documentation, and testing skills.
Experience and education
Requirements
Experience architecting or maintaining substantial, complex, and large software projects.
Experience using coding agents for long tasks.
Preferred qualifications
Background in code review, test authoring, or maintaining production and/or open-source systems.
Exposure to AI evaluation, rubric/data grading, or prompt writing.
Equipment requirements
Access to a large, complex, and private codebase you own and can author tasks against.
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.