Help shape the future of AI by designing challenging mechanical engineering problems that test and improve reasoning in cutting-edge models.
Location: Remote
Type: Contract / Part-time
Commitment: 20 hours per week
Compensation: Up to 40 USD / hr
Project duration: 2 months, with potential extension
Availability: Immediate start
We create high-quality STEM training data for frontier AI models. Our data is used directly in training and evaluation pipelines at leading AI labs to improve model reasoning in technical domains.
We are looking for experts in Mechanical Engineering to design rigorous, deterministic problems that are genuinely challenging for state-of-the-art AI systems. Each problem must have exactly one verifiable correct answer and be submitted together with a complete, verified solution.
Design advanced mechanical engineering problems for frontier AI training and evaluation
Create deterministic problems with exactly one correct answer
Write complete, verified solutions and clearly document the reasoning process
Develop problems that test engineering analysis and multi-step reasoning, not just memorization
Work across areas such as mechanics, thermodynamics, fluid mechanics, heat transfer, dynamics, and mechanical systems
Where relevant, use Python, MATLAB, CAD/CAE tools, or specialized engineering software to validate solutions
Ensure all outputs are technically precise, reproducible, and well-written in English
Master’s or PhD in Mechanical Engineering or a closely related field
Strong research or industry experience involving mechanical systems, engineering analysis, simulation, or mathematical modeling
Strong quantitative and problem-solving skills
Familiarity with numerical methods and computational engineering
Ability to design original, difficult problems that mirror real mechanical engineering workflows
Excellent attention to detail and technical writing skills in English
Experience with MATLAB, Python, ANSYS, Abaqus, SolidWorks, COMSOL, or similar tools
Background in finite element analysis, computational fluid dynamics, optimization, or simulation
Experience designing technical assessments, evaluating model reasoning, or developing engineering educational content