The work
You will help improve AI systems by creating demanding hardware and engineering tasks, then checking whether model-generated work is technically sound. The role combines practical engineering judgment with structured evaluation and clear standards for reliable results.
- Create realistic tasks involving design, analysis, simulation, testing, manufacturing, debugging, or system integration.
- Write task instructions, reproducible environments, reference solutions, and evaluation criteria.
- Review model-generated calculations, code, simulations, analyses, and other technical deliverables.
- Find invalid assumptions, incorrect results, integration failures, and reproducibility problems.
- Set objective standards for technically correct and reliable work.
What it pays and takes
This is a fully remote, part-time contractor role for an expert in at least one hardware or engineering discipline. Work is flexible, but task availability and completion time may vary.
- Pay: $60 to $120 per hour equivalent, paid per task that meets the project specifications.
- Schedule: Less than 20 hours per week.
- Location: Open to candidates in the countries listed for this role, including the United States, Canada, the United Kingdom, India, Japan, and other eligible locations.
- Language: Professional fluency in English.
- Experience: Prior paid experience training, evaluating, benchmarking, or improving AI systems.
- Technical skills: Strong practical proficiency with Python or comparable engineering scripting.
- Engineering background: Hands-on expertise in at least one hardware or engineering discipline, such as mechanical, electrical, embedded, civil, chemical, manufacturing, robotics, materials, energy, or biomedical engineering.
- Tools: Experience with engineering software, command-line tools, configuration files, scripting interfaces, or programmatic APIs.
- Work samples: You should be able to show public technical work, such as production designs, validated simulations, diagnosed failures, patents, publications, open-source contributions, schematics, PCB designs, RTL, test systems, scripts, or technical projects.
Helpful background
Experience creating AI-training tasks, reference solutions, rubrics, graders, or benchmark problems can help. Experience reviewing other AI trainers' technical submissions is also useful.
- Verification and validation
- Testing and failure analysis
- Technical quality assurance
- Long-horizon or agentic AI evaluations
- Advanced academic or professional engineering work
How it works
You apply here in about two minutes and finish on the hiring platform. The next step is a short AI interview (technical roles include a coding challenge). Passing it is how you qualify for paid tasks, so take it soon after you are invited.
The hiring platform's application for this job asks for your resume, your LinkedIn profile, your phone number, your location and answers to 8 screening questions.
About AI training work
AI training is the human work behind systems that can generate and analyze information. People with strong technical experience create examples, review model outputs, and explain what makes an answer correct, helping AI systems perform more reliably on specialist work.