Biomedical Engineering QA Lead - Remote

About the role

This role involves overseeing quality, consistency, and trainer performance across biomedical engineering AI training projects. You will review AI-generated content, provide feedback, and ensure adherence to quality standards.

Responsibilities

• Spot-check biomedical engineering items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
• Evaluate AI-generated biomedical engineering explanations, medical-device reasoning, biomechanics calculations, biomaterials discussions, bioinstrumentation workflows, biosignal explanations, diagrams/descriptions, and problem-solving steps for correctness and clarity.
• Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and biomedical-engineering-specific review standards.
• Respond to trainer/QA questions clearly and promptly, especially around engineering assumptions, units, formulas, biological context, device safety, regulatory considerations, standards references, and rubric interpretation.
• DM contributors who are inactive or not working, encourage activation, track follow-ups, and flag availability issues when needed.
• Create and maintain biomedical engineering project documentation, including style guides, trackers, FAQs, quality notes, examples, honeypots, calibration tasks, and onboarding materials.
• Schedule and run onboarding/training calls with trainers and QAs to explain project expectations, workflows, rubrics, quality standards, and biomedical-engineering-specific review requirements.
• Ensure all trainers and QAs apply biomedical engineering guidelines consistently and understand updates as projects evolve.
• Flag unsafe, misleading, or overconfident biomedical engineering recommendations, especially where medical devices, patient safety, clinical workflows, biological systems, diagnostics, imaging, rehabilitation tools, or regulatory claims may be affected.
• Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for biomedical engineering AI training projects.

Requirements

• Bachelor’s or Master’s degree in Biomedical Engineering, Bioengineering, Medical Engineering, Biomechanical Engineering, Electrical Engineering with biomedical focus, Mechanical Engineering with biomedical focus, or a closely related 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 biomedical engineering, medical devices, biomechanics, biomaterials, bioinstrumentation, clinical engineering, R&D, regulatory documentation, technical review, engineering education, or related workflows.
• Strong understanding of core biomedical engineering topics such as biomechanics, biomaterials, medical devices, bioinstrumentation, biosignals, imaging systems, physiological systems, tissue engineering, rehabilitation engineering, and biomedical data analysis.
• Ability to evaluate biomedical engineering content against detailed rubrics and identify issues such as incorrect assumptions, flawed calculations, missing units, unsafe recommendations, weak biological/clinical reasoning, hallucinated standards, regulatory overclaims, or incomplete explanations.
• Familiarity with common biomedical engineering tools or workflows such as MATLAB, Python, LabVIEW, SolidWorks, CAD/CAE tools, signal processing workflows, medical device documentation, ISO/FDA-related documentation, clinical engineering workflows, or biomedical data analysis 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, biomedical content QA, or rubric-based LLM evaluation is a strong plus.