[Remote] Mechanical Engineer Quality Assurance Lead (QAL)

Note: The job is a remote job and is open to candidates in USA. SME Careers is a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies. They are seeking a Mechanical Engineering Quality Assurance Lead (QAL) to oversee quality and consistency across mechanical engineering AI training projects, reviewing AI-generated content and providing feedback to ensure adherence to quality standards.ResponsibilitiesQuality monitoring: Spot-check mechanical engineering items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issuesTechnical review: Evaluate AI-generated engineering explanations, calculations, design recommendations, diagrams/descriptions, and problem-solving steps for correctness and clarityTrainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and engineering-specific review standardsQuestion handling: Respond to trainer/QA questions clearly and promptly, especially around engineering assumptions, units, formulas, calculations, safety concerns, standards references, and rubric interpretationTrainer/QA activation management: DM contributors who are inactive or not working, encourage activation, track follow-ups, and flag availability issues when neededDocumentation: Create and maintain mechanical engineering project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding materialsOnboarding 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 requirementsQuality alignment: Ensure all trainers and QAs apply engineering guidelines consistently and understand updates as projects evolveRisk and safety review: Flag unsafe, misleading, or overconfident engineering recommendations, especially where design, manufacturing, equipment, structural integrity, or operational safety may be affectedProcess improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for mechanical engineering AI training projectsSkillsBachelor's or Master's degree in Mechanical Engineering, Aerospace Engineering, Mechatronics, Manufacturing Engineering, or a closely related engineering fieldStrong grasp of the English language to follow project guidelines, communicate with teams, and provide clear technical feedback in English3+ years of professional experience in mechanical engineering, product design, manufacturing, R&D, systems engineering, CAD, simulation, technical review, engineering education, or related workflowsStrong understanding of core mechanical engineering topics such as mechanics, thermodynamics, fluid mechanics, heat transfer, machine design, materials, manufacturing processes, dynamics, statics, and engineering drawing interpretationAbility 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 explanationsComfortable working in fast-moving remote environments using tools such as Discord, Google Sheets, Google Docs, trackers, dashboards, and project management systemsHighly detail-oriented and organized, with the ability to maintain style guides, FAQs, trackers, onboarding materials, honeypots, calibration tasks, and other quality documentationFamiliarity with common engineering tools or workflows such as CAD, FEA/CAE, MATLAB, Python, SolidWorks, AutoCAD, ANSYS, Fusion 360, or similar toolsExperience leading or supporting remote teams of trainers, annotators, reviewers, engineers, technical writers, or QAsExperience with AI training, data annotation, large language models, prompt/response evaluation, technical content QA, or rubric-based LLM evaluationCompany OverviewSME Careers by SuperAnnotate connects subject-matter experts, students, and professionals with flexible, remote AI training work such as annotation, evaluation, fact-checking, and content review. It was founded in undefined, and is headquartered in San Francisco, California, US, with a workforce of 11-50 employees. Its website is https://sme.careers/.