AI Trainer – Mechanical Engineering

Planet Pharma; San Francisco (CA); Remote; USD 90,000 - 150,000; Part time

Job summary

Planet Pharma seeks an experienced mechanical engineer to design realistic, practice-driven tasks for evaluating frontier AI models. You will craft prompts, select drawings and data, and oversee evaluation against professional standards.

Remote work is supported and tasks emphasize real-world judgment and detailed written reasoning. The role requires 2+ years in mechanical engineering, strong CAD/FEA knowledge, and excellent English communication.

Qualifications

• 2+ years of professional mechanical engineering experience.
• Fluent written and spoken English.
• Meticulous attention to detail and strong problem solving.

Responsibilities

• Design challenging, realistic mechanical engineering tasks drawn from your own day-to-day work; create prompts, files, and data needed.
• Run tasks through frontier AI models and evaluate against professional standards.
• Compare model outputs on identical prompts and files; document which performed better and why.
• Write closed-ended problems with worked solutions and grading rubrics; specify correct deliverables and acceptance criteria.
• Flag concrete failures with evidence: boundary conditions, unit errors, interferences, tolerance issues, unsafe factors of safety.

Skills

Experience

English proficiency

Attention to detail

Self-direction

Education

Bachelor's in mechanical engineering or related

Tools

CAD software

FEA/CFD

MATLAB/Python

Job description

About the role

is looking for experienced mechanical engineers to evaluate how frontier AI models handle real engineering work: sizing a component against a real load case, reading a CAD assembly and spotting what interferes at full travel, a tolerance stack up, a thermal or fluids calculation, a DFM decision, a failure analysis. The models can already talk fluently about mechanical engineering; what they cannot yet do reliably is the actual work. You bring the judgment you have built catching the failure mode the spec sheet does not mention. We bring the model output that judgment is needed to grade.

In this role, you will design challenging, realistic tasks drawn from your own practice, such as a component sizing calculation with a worked solution, a tolerance stack up, a CAD assembly review with interference and clearance findings, a thermal or fluids analysis, a DFM and DFA review, a failure analysis report, or a test and validation plan, run them through frontier AI agents, and evaluate what comes back against a professional standard.

You will work with realistic professional files, the kind a practitioner in your field actually handles, which you assemble yourself. Some tasks are compact, built around a handful of files; others are larger scenarios that take several days to build. In every case the goal is the same: a task a competent professional in your field would complete correctly and a frontier model currently gets wrong.

This is not a traditional mechanical engineering role. You will be helping build better AI by putting your knowledge to work in a structured, flexible, fully remote environment. The work is long form and self directed, and clear written reasoning matters as much as technical depth.

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.

Domain qualifications

• 2+ years of professional mechanical engineering experience preferred. Any specialty: design and product development, manufacturing, thermal, structural, automotive, aerospace, HVAC, robotics, energy, medical devices.
• In progress Bachelor's degree or higher in mechanical engineering or a related field. PE licensure is a plus but not required.
• 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.

General requirements

• 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.
• 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: compatible 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).
• Available at least 10 hours per week, with no weekly maximum. Consistent availability is valued and full time hours are available.
• Based in the United States, Canada, or the UK.