AI Trainer (SWE)

$60–120/hr

rate as listed

Workload: 15+ hrs/week
Engagement: Project-based
Location: Worldwide
Field: Generalist

Create reproducible software engineering tasks and evaluate model-generated code for correctness, robustness, and completeness. This remote contractor role requires prior paid AI-training or model-evaluation experience, strong Python proficiency, and professional experience in another programming language.

The work

You will design realistic software engineering tasks for advanced AI systems, including clear instructions, reproducible environments, reference solutions, tests, and objective evaluation criteria. Day to day, you may inspect model-generated code, reproduce failures, debug dependencies, test edge cases, and determine whether a submission is correct, robust, complete, and reproducible.

What micro1 screens for

The AI interview includes technical and integrity checks. Expect follow-up on prior paid AI-training or model-evaluation work, specific repositories or technical artifacts, and decisions you made when grading code. Python is required. Professional experience with at least one additional programming language is also required, with Java, JavaScript, TypeScript, Rust, C++, C#, and Go listed as examples. Experience with benchmark construction, graders, Linux, Docker, CI/CD, command-line tools, sandboxed environments, programming competitions, or long-horizon and agentic coding evaluations is preferred rather than required.

Logistics

This is a global, fully remote contractor role with a flexible schedule and a commitment of at least 15 hours per week. Work is task-based and largely asynchronous, although the listing does not specify meeting expectations. Compensation is output-based and depends on tasks meeting project specifications and quality requirements, so task availability and completion time may vary.

Pay band: $60–120/hr

What they look for

Software Engineering
AI training

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: This verifies that your experience involved substantive paid work on AI systems and distinguishes your own contributions from the broader team’s output.
A strong answer: Name the project context, your exact responsibilities, and concrete artifacts such as tasks, tests, graders, reference solutions, review decisions, or benchmark results. Include a verifiable detail or public artifact where confidentiality permits.
Where people lose it: Candidates lose points by describing general software engineering or personal prompt experimentation without showing paid AI-training, benchmarking, evaluation, or improvement work.

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