Mechanical Engineering QA Lead - Remote

Key responsibilities

• Quality monitoring: Spot-check mechanical engineering items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
• Techical 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.

About the role

In this hourly, remote contractor role, you will work as a Mechanical Engineering Quality Assurance Lead 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.