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Mechanical Engineer Quality Assurance Lead (QAL)

SME Careers • Remote

Education

Master's

Type

Hourly

Pay Rate

$75/hr

Listed

53d ago

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What We Know About This Role

Interview
Required i As stated in the listing: “Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.”

About this role

From the SME Careers listing

In this hourly, remote contractor role, you will work as a Mechanical Engineering Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across mechanical engineering AI training projects. You will review AI-generated mechanical engineering content and trainer/QA work, evaluate output quality against project guidelines, provide precise written feedback, and ensure that all contributors follow the expected quality standards.

You will assess work for technical accuracy, engineering reasoning, calculation correctness, standards awareness, clarity, safety considerations, unit consistency, formatting, instruction-following, and adherence to project-specific rubrics. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently. This role requires strong mechanical engineering expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote technical teams.

This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your mechanical engineering quality leadership will directly help improve the world’s premier AI models by ensuring that engineering training data is accurate, logically sound, clearly explained, well-documented, and aligned with client expectations.

Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.

Important: There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.

Key responsibilities

  • Quality monitoring: Spot-check mechanical engineering items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
  • Technical review: Evaluate AI-generated engineering explanations, calculations, design recommendations, diagrams/descriptions, and problem-solving steps for correctness and clarity.
  • Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and engineering-specific review standards.
  • Question handling: Respond to trainer/QA questions clearly and promptly, especially around engineering assumptions, units, formulas, calculations, safety concerns, standards references, and rubric interpretation.
  • Trainer/QA activation management: DM contributors who are inactive or not working, encourage activation, track follow-ups, and flag availability issues when needed.
  • Documentation: Create and maintain mechanical engineering project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding materials.
  • Onboarding and training: Schedule and run onboarding/training calls with trainers and QAs to explain project expectations, workflows, rubrics, quality standards, and mechanical-engineering-specific review requirements.
  • Quality alignment: Ensure all trainers and QAs apply engineering guidelines consistently and understand updates as projects evolve.
  • Risk and safety review: Flag unsafe, misleading, or overconfident engineering recommendations, especially where design, manufacturing, equipment, structural integrity, or operational safety may be affected.
  • Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for mechanical engineering AI training projects.

Your profile

  • Bachelor’s or Master’s degree in Mechanical Engineering, Aerospace Engineering, Mechatronics, Manufacturing Engineering, or a closely related engineering field.
  • Strong grasp of the English language to follow project guidelines, communicate with teams, and provide clear technical feedback in English.
  • 3+ years of professional experience in mechanical engineering, product design, manufacturing, R&D, systems engineering, CAD, simulation, technical review, engineering education, or related workflows.
  • Strong understanding of core mechanical engineering topics such as mechanics, thermodynamics, fluid mechanics, heat transfer, machine design, materials, manufacturing processes, dynamics, statics, and engineering drawing interpretation.
  • Ability to evaluate engineering content against detailed rubrics and identify issues such as incorrect assumptions, flawed calculations, missing units, unsafe recommendations, poor reasoning, hallucinated standards, or incomplete explanations.
  • Familiarity with common engineering tools or workflows such as CAD, FEA/CAE, MATLAB, Python, SolidWorks, AutoCAD, ANSYS, Fusion 360, or similar tools is preferred.
  • Experience leading or supporting remote teams of trainers, annotators, reviewers, engineers, technical writers, or QAs is strongly preferred.
  • Comfortable working in fast-moving remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systems.
  • Highly detail-oriented and organized, with the ability to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and other quality documentation.
  • Experience with AI training, data annotation, large language models, prompt/response evaluation, technical content QA, or rubric-based LLM evaluation is a strong plus.

🌍 Geographic Availability This role is available to qualified experts globally. However, as a U.S.-based company, SME Careers complies with U.S. export control and sanctions regulations. Eligibility depends on your country of residence and any applicable trade restrictions. Some projects may have additional language or region-specific requirements.

💰 Pay Rate May Vary Based on Your Location Your actual compensation will be determined based on your location and local market conditions. See regional job listings for localized pay information, or the application process will clarify your specific rate.

If you're unsure whether you're eligible, proceed with the application—SME's screening will determine your jurisdiction status and applicable compensation.

How long hiring takes

Across the AI training platforms we refer candidates to, the median gap between referral and hire is about 30 days. It varies by platform and role, so treat it as a rough guide for this one.

Within 2 weeks
~25%
Within 6 weeks
~60%
Within 3 months
~80%

Why this role

This Mechanical Engineering QAL role pays $75/hr to oversee quality across mechanical engineering AI training projects. A degree in mechanical, aerospace, or mechatronics engineering is required, along with three or more years of product design, manufacturing, or R&D experience. Someone who has spent years signing off on other engineers' calculations will find that same review instinct is what this role is built around.

Talent pool

We're light on STEM candidates

We've matched 65 people with a STEM background against 726 STEM listings we've tracked, so most go out without one. Set up a profile and we'll consider you for a role like this one.

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Common questions

How does SME Careers pay freelancers?

Weekly, through Deel. Deel also handles tax compliance, which is how SME Careers pays contractors across more than 40 countries without running its own international payroll.

Is SME Careers work ongoing or project-based?

Project-based. Assignment duration and availability vary by project, but consistently high-quality work earns priority access to whatever comes next.

Is academic-niche AI training a continuous job?

Usually not. Work runs in campaigns, like training a model specifically on quantum mechanics, that might last a few weeks. Treat it as a high-paying fellowship or grant rather than a permanent daily job.

Is academic-niche AI training just data labeling?

No, it's closer to academic research. Expect to write or verify complex proofs, solve advanced equations, or check the logic behind a model's step-by-step reasoning. The goal is teaching AI systems to reason deeply within your specific field.

What does STEM work look like for a Mechanical Engineer Quality Assurance Lead (QAL)?

Tasks here are scoped to STEM, not generic labeling. As a Mechanical Engineer Quality Assurance Lead (QAL), expect to draw on real domain judgment (evaluating outputs, correcting errors, or providing expert reasoning specific to STEM) rather than following a one-size-fits-all rubric. If you don't have hands-on STEM background, this is likely not the right listing to start with.

What specific skills does this listing call for?

Mechanical Engineering, AI Training, LLM evaluation, and Technical Review are named directly in the listing. If you don't have hands-on experience with these, expect the screening process to test for them directly rather than accepting adjacent experience as a substitute.

How much does this specific role pay?

This listing is posted at $75/hr, a per-task rate. Pay can change between when we last checked the listing and when you apply, so confirm the current number on the platform's own application page before committing time.

What happens when I click Apply on this listing?

You'll be taken to SME Careers's external site to complete your application there. This listing links through a referral, but the process is identical to applying directly; the link just routes you correctly. Create an account on their site and follow their onboarding steps.

Do I need a Master's to qualify?

This role lists a master's degree as a requirement. In practice, the domain assessment is the real gate. If you can pass it, the degree is usually secondary. However, some platforms verify credentials formally, so list your actual qualifications accurately on your profile.

Is there an interview?

Yes. Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.

Who is behind SME Careers?

SME Careers is the expert talent division of SuperAnnotate, an AI data infrastructure platform, connecting domain experts with high-level AI training projects.