AI Trainer - Mechanical Engineering

Planet Pharma is seeking experienced mechanical engineers to help evaluate and improve frontier AI models on real mechanical engineering work. The role involves designing realistic engineering tasks, running them through AI models, evaluating outputs against professional standards, documenting failures, and contributing to design, analysis, manufacturing, and testing reviews in a fully remote environment.

Responsibilities:

• Design challenging, realistic mechanical engineering tasks drawn from your own day to day work: the scenario, a prompt phrased the way you would brief a trusted colleague, and the supporting files an engineer would need (drawings, CAD models, load cases, material data, test data, specifications), which you author yourself
• Run those tasks through frontier AI models and evaluate the deliverable they produce (the calculation, design review, analysis or report) against the standard you would hold a colleague to
• Compare two model outputs on identical prompts and files, decide which performed better, and document where each fell short
• Write closed ended problems with worked solutions and acceptance criteria, and detailed grading rubrics that specify what a correct deliverable must contain, such as the right loads and factors, the right material, the right tolerances and the right failure modes considered, and explain in writing why a response passes or fails each one
• Flag concrete failures with evidence: wrong boundary conditions, unit errors, interferences missed, tolerance stacks that do not close, unsafe factors of safety, fabricated or ignored source files, and off brief interpretation of the ask
• Contribute across design, analysis, manufacturing and test, and review and refine tasks built by other engineers

Requirements:

• In progress Bachelor's degree or higher in mechanical engineering or a related field
• Depth in at least one of: mechanical design and CAD (geometry, clearances, tolerances, mechanisms); structures and materials (stress, deflection, fatigue, buckling, material selection, failure analysis); thermal and fluids (heat transfer, thermodynamics, HVAC, pumps and piping); dynamics and controls (vibration, kinematics, rotating machinery); manufacturing (DFM and DFA, process selection, GD&T, tolerance stack ups, cost and quality trade offs)
• Fluency in the tools of your specialty: CAD (SolidWorks, Fusion 360, Onshape, Creo, NX, CATIA or Inventor), FEA or CFD, or MATLAB or Python for engineering analysis
• Strong fundamentals across mechanics of materials, dynamics, thermodynamics, heat transfer, fluid mechanics and manufacturing processes
• Meticulous: you check your own work and enjoy finding the edge case that breaks an assumption
• Able to draw on your own real world experience and day to day workflows to craft scenarios that test whether an AI system can actually do the work
• Hands on practitioner: you currently do (or recently did) the work yourself at an individual contributor level, not solely in a managerial capacity
• Full professional or native level written and spoken English, with strong written communication. You can explain complex professional reasoning clearly and concisely, and articulate why a result is wrong, not only that it is
• Comfort with ambiguity and attention to detail. You can orient in a new set of files and build an accurate, deep working picture of it quickly, especially when the subject sits partly outside your own specialization. You verify what a document claims against the underlying numbers, sources or facts
• Capable of interpreting feedback, judging which parts of it are actually correct, and applying it without hand holding. When stuck, you look for the answer rather than waiting for one
• Ability to ramp quickly on unfamiliar work from written material and instructions alone, including where that material is incomplete (for example, writing grading rubrics for the first time)
• General familiarity with AI and LLM tools. You have used models like Claude or ChatGPT in professional work and have the judgment to tell a well reasoned answer from a plausible sounding but incorrect one
• Baseline tech literacy: comfortable with cloud file tools (e.g., Google Workspace), managing browser profiles, downloading and installing desktop apps (e.g., Claude), and everyday file handling (e.g., converting between Excel and Google Sheets, zipping files for sharing)
• 2+ years of professional mechanical engineering experience preferred
• 2+ years of hands on experience in your field preferred (see Domain qualifications above). Candidates with less experience are considered where the practical work is real
• PE licensure