Mechanical Engineering QA Lead - Remote
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Key skills for this role
About the Role
YO IT Consulting is seeking a Mechanical Engineering QA Lead for a remote contract role. The position involves overseeing quality and trainer performance on AI training projects, reviewing mechanical engineering content, and providing feedback.
Key Skills for This Role
Responsibilities
- Spot check mechanical engineering items and identify quality issues
- Evaluate AI generated engineering explanations, calculations, design recommendations, and problem solving steps for correctness and clarity
- Update trainers and QAs on project guidelines and quality expectations
- Respond to trainer/QA questions clearly and promptly
- DM contributors who are inactive to encourage activation
- Create and maintain mechanical engineering project documentation
- Schedule and run onboarding/training calls with trainers and QAs
- Ensure all trainers and QAs apply engineering guidelines consistently
- Flag unsafe, misleading, or overconfident engineering recommendations
- Identify recurring quality gaps and propose workflow improvements
Requirements
- Bachelor's or Master's degree in Mechanical Engineering, Aerospace Engineering, Mechatronics, Manufacturing Engineering, or a closely related field
- 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 grasp of the English language
- Strong understanding of core mechanical engineering topics
- Ability to evaluate engineering content against detailed rubrics
- Experience leading or supporting remote teams is strongly preferred
- Familiarity with common engineering tools or workflows such as CAD, FEA/CAE, MATLAB, Python, SolidWorks, AutoCAD, ANSYS, Fusion 360, or similar tools is preferred
Full Job Posting
About This 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.
- 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.
- 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.
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.
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.
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