Engineering Expert, Simulation Design

You will design rigorous engineering simulation and coding tasks that train and evaluate AI agents. The work spans electrical, mechanical, and aerospace engineering and combines engineering judgment, Python development, open-source simulation, and objective evaluation.

You will also review agent behavior across repeated trials, identify technical failures, and help improve benchmark difficulty without losing clear requirements or physically valid solutions.

• Write self-contained engineering problems with competing constraints, optimization goals, validated reference solutions, and objective autograders.
• Build, run, and validate simulation environments with open-source tools and custom Python test benches.
• Review coding-agent outputs and execution logs for issues such as misunderstood simulator feedback, early convergence, and physically impossible geometries.
• Use model performance results to calibrate benchmark difficulty while preserving technically valid solutions.
• Work with AI researchers, pod leads, and engineering experts to add high-rigor benchmarks to evaluation workflows.

What it pays and takes

This is a remote contractor engagement lasting up to 12 weeks. Part-time work is accepted, and the structured schedule calls for 20 or more hours per week, with work potentially reaching 40 hours per week.

The role requires strong engineering judgment, Python development, simulation experience, and familiarity with AI evaluation. Candidates should be fluent in English.

• Remote worldwide engagement.
• Contractor role lasting up to 12 weeks.
• 20 or more hours per week, with up to 40 hours per week possible.
• Four hours of overlap with Pacific Time required.
• Weekend on-call availability required.
• Bachelor's degree or higher in electrical, mechanical, aerospace, or a closely related engineering field.
• At least five years of hands-on engineering design experience.
• Strong Python skills and experience with at least one relevant open-source simulator, such as ngspice, OpenFOAM, FEniCSx, CalculiX, python-control, CadQuery, OpenModelica, or Gmsh.
• Experience with LLMs or coding agents, including concepts such as pass@k, trajectory-log auditing, and systematic failure diagnosis.
• Careful use of physical plausibility checks, unit consistency, boundary conditions, convergence criteria, and technical documentation.
• Experience in AI evaluation, data annotation, content review, quality assurance, or a related analytical role.

How it works

Apply on OpenTrain with your resume, then complete the application on the hiring site.

About AI training work

AI training is the human work behind systems that learn from examples, including writing, reviewing, and evaluating model outputs. Experienced contributors are paid for applying specialized judgment that helps make AI systems more accurate, useful, and reliable.