Mechanical Engineer for AI Training Program

Brunel is partnering with DataAnnotation to connect experienced Mechanical Engineers with an innovative AI training program. This opportunity brings together hands-on mechanical engineering expertise and artificial intelligence to help evaluate and improve how AI models solve real-world engineering problems. Selected experts will contribute their knowledge to the development of high-quality, technically accurate AI systems.

This opportunity is designed for practicing engineers across mechanical design and CAD, structures and materials, thermal and fluid systems, dynamics and controls, manufacturing, automotive, aerospace, HVAC, robotics, energy, and related mechanical engineering disciplines.

The models can already communicate fluently about mechanical engineering concepts. The challenge is teaching them to reliably perform the actual engineering work: sizing components against real load cases, identifying potential failure modes, evaluating CAD assemblies and clearances, analyzing design changes, and understanding downstream impacts. Your engineering judgment will help establish the benchmark against which these AI models are evaluated.

Engagement Details

• Fully remote, project-based work with a flexible schedule. Around 10 hours per week is the recommended starting point; the project prioritizes engineers who can commit 30–40 hours per week, though part-time availability is considered.
• Pay Rate: $40–$125+ USD/hour, paid via PayPal
• Location: United States, Canada, United Kingdom, Ireland, Australia, and New Zealand

First Step

Skills assessment (1–3 hours)

Responsibilities

• Design challenging, closed-ended mechanical engineering problems based on your own professional experience that require genuine engineering reasoning rather than simple information lookup.
• Derive and verify the correct answers to your problems and develop clear, technically rigorous worked solutions demonstrating your methodology.
• Test engineering problems against advanced AI models and refine them to appropriately challenge the models’ reasoning capabilities.
• Evaluate AI-generated mechanical engineering analysis, designs, calculations, and technical documentation for accuracy and real-world applicability.
• Compare AI-generated solutions and determine which performs better, identifying specific strengths, weaknesses, and technical errors.
• Identify and document concrete failures, including incorrect assumptions, calculation or unit errors, unrealistic results, overlooked failure modes, improper material or component selection, and misinterpretation of engineering requirements.
• Review problems and solutions developed by other engineering contributors for technical correctness, ambiguity, rigor, and practical relevance.
• Apply your expertise across one or more mechanical engineering disciplines, including:
  • Mechanical Design & CAD: geometry, clearances, tolerances, mechanisms, assemblies, and the impact of design changes.
  • Structures & Materials: stress, deflection, fatigue, buckling, material selection, and failure analysis.
  • Thermal & Fluids: heat transfer, thermodynamics, HVAC, pumps, piping, and fluid mechanics.
  • Dynamics & Controls: vibration, kinematics, rotating machinery, and control systems.
  • Manufacturing: DFM/DFA, process selection, GD&T, tolerance stack-ups, and cost and quality trade-offs.