AI Trainer (Engineering Sciences)

The work

You will create challenging engineering tasks and review technical work produced by AI models. The work uses practical engineering judgment to assess whether calculations, simulations, code, designs, and other deliverables are correct, reliable, and reproducible.

You may work across mechanical, electrical, embedded, civil, chemical, manufacturing, robotics, materials, energy, or biomedical engineering. Tasks can involve design, analysis, simulation, testing, manufacturing, debugging, or system integration.

• Write realistic engineering task instructions.
• Build reproducible environments and reference solutions.
• Create clear evaluation criteria, rubrics, graders, or benchmark problems.
• Review model-generated calculations, code, simulations, analyses, and technical deliverables.
• Identify invalid assumptions, incorrect results, integration failures, and reproducibility problems.
• Set objective standards for technically correct and dependable work.

What it pays and takes

This is a fully remote, part-time contractor role for an expert engineering contributor. Work is output-based and paid per accepted task.

• $60 to $120 per hour equivalent, paid per task that meets the project specifications.
• Flexible schedule with a commitment of at least 15 hours and less than 20 hours per week.
• Open to candidates in multiple countries, including the United States, Canada, the United Kingdom, India, Japan, and other eligible locations listed for the role.
• Professional fluency in English.
• Prior paid experience training, evaluating, benchmarking, or improving AI systems.
• Strong practical proficiency with Python or comparable engineering scripting.
• Hands-on expertise in at least one hardware or engineering discipline.
• Experience with engineering software, command-line tools, configuration files, scripting interfaces, or programmatic APIs.
• Public technical work such as production designs, validated simulations, failure investigations, 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 is useful. Reviewing technical submissions from other AI trainers can also help.

Additional relevant experience includes verification and validation, testing, failure analysis, technical quality assurance, long-horizon or agentic AI evaluations, and 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 learn from examples, including creating tasks and reviewing model responses. People with engineering expertise are needed to judge whether technical AI outputs are accurate, useful, and safe.