Training Turk

Mercor listing

Mechanical Engineer

$60–90/hr

The role in one line

A mechanical engineer who evaluates and improves how frontier AI models reason about real engineering design, analysis, and manufacturing work.

Written by Training Turk from the public listing; it may be incomplete or out of date. Read the full posting on Mercor.

What you would do

  • Review mechanical engineering tasks and model outputs for correctness, unsafe assumptions, and constraint violations
  • Write instruction specifications and worked reference solutions defining what correct engineering looks like
  • Design challenging benchmarks and evaluation sets across thermal, structural, manufacturing, and controls domains
  • Work with research teams to translate engineering judgment into teachable, explicit criteria
  • Assess whether AI models can reason through real design decisions, not just textbook problems

Who they are looking for

  • Bachelor's degree in mechanical engineering or closely related discipline
  • 5+ years hands-on professional mechanical engineering experience with genuine project ownership
  • Fluency with common CAD and analysis tools (SolidWorks, CATIA, NX, ANSYS, MATLAB) and standards (ASME, ASTM, ISO, GD&T)
  • Clear career progression toward senior or lead engineering responsibility
  • Hands-on experience with large language models and ability to distinguish sound reasoning from plausible errors

Skills this role asks for

finite element analysiscomputational fluid dynamicscad modeling and designthermal and fluid systemsstructural analysis and stressmanufacturing process knowledgeasme and design standardsreference solution developmentengineering qa and standardsinstruction specification writingbenchmark designdomain-specific evaluation tools

What the interview is likely to probe

  1. 1.Vetting model output quality

    A model that sounds confident but contains hidden assumption errors will produce misleading answers; catching these requires deep judgment.

    Expect something like: “A model designs a pressure vessel but ignores safety factors and fatigue. What hidden errors does this miss, and how would you test for these gaps?”

  2. 2.Reference solution development

    Teaching AI requires explicit correct answers with reasoning; vague or incomplete reference solutions produce poor training signals.

    Expect something like: “Write a reference solution for a structural analysis problem involving a cantilevered beam under combined loading. What assumptions would you make explicit? What work steps would you require to demonstrate real engineering judgment?”

  3. 3.Benchmark design across domains

    Benchmarks must span real engineering practice; simple problems miss where models actually fail versus where they excel.

    Expect something like: “Design 5 benchmark problems that test whether a model understands thermal management in electronic enclosures. How would these problems expose gaps between textbook knowledge and practical design?”

  4. 4.Translating judgment to teachable criteria

    Models can't learn from vague expertise; converting gut engineering feel into explicit rules is how you make models improve.

    Expect something like: “When reviewing a design, you immediately see it won't manufacture efficiently. How would you break this intuitive judgment into explicit, teachable rules that an AI model could learn and apply?”

  5. 5.Manufacturing and design reality

    Real engineering balances multiple constraints; models trained only on analysis miss practical manufacturability and cost issues.

    Expect something like: “A model recommends a design that's theoretically optimal but requires exotic materials and precision machining. How would you evaluate this response, and what benchmark would catch this gap?”

Exercise you may get

Review 3 mechanical engineering task proposals and 3 model-generated solutions, identify technical errors and gaps, then write one complete instruction spec and reference solution for a design problem combining multiple constraints.

How to prepare

  • Review recent AI model outputs on engineering tasks to understand common failure modes
  • Prepare 2-3 examples from your own work showing how real constraints differ from textbook problems
  • Reflect on how you'd explain engineering judgment non-verbally (design review meetings) in explicit written criteria
  • Study how to structure reference solutions with clear assumptions, constraints, and reasoning

Facts

Pay
$60–90/hr
Commitment
full-time
Hours
40 per week
Work arrangement
remote · United States
Eligible locations
USA
Domain
Other Engineering
Posted
9/16/2026
Open slots
20