[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.

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

Skills

• 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
• 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
• Familiarity with common engineering tools or workflows such as CAD, FEA/CAE, MATLAB, Python, SolidWorks, AutoCAD, ANSYS, Fusion 360, or similar tools
• Experience leading or supporting remote teams of trainers, annotators, reviewers, engineers, technical writers, or QAs
• Experience with AI training, data annotation, large language models, prompt/response evaluation, technical content QA, or rubric-based LLM evaluation

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