Biomedical Engineering Quality Assurance Lead (QAL)

In this hourly, remote contractor role, you will work as a Biomedical Engineering Quality Assurance Lead (QAL) to oversee quality, consistency, and trainer performance across biomedical engineering AI training projects. You will review AI-generated biomedical 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, biomedical reasoning, calculation correctness, standards awareness, regulatory awareness, unit consistency, safety considerations, clarity, formatting, instruction-following, and adherence to project-specific rubrics. You will spot recurring quality issues, communicate updates to trainers and QAs, support onboarding, maintain documentation, and help activate contributors who are not working consistently. This role requires strong biomedical engineering expertise, strong English communication skills, excellent attention to detail, structured communication, and the ability to manage quality workflows across remote technical teams.

This role is with SME Careers, a fast-growing AI Data Services company and subsidiary of SuperAnnotate, delivering training data for many of the world’s largest AI companies and foundation-model labs. Your biomedical engineering quality leadership will directly help improve the world’s premier AI models by ensuring that biomedical engineering training data is accurate, logically sound, clearly explained, well-documented, safety-aware, and aligned with client expectations.

Selection process involves an AI interview, a domain-specific task, and an interview with a recruiter.

Important:
There is no immediate project for this role; however, if qualified, you will be among the first experts we reach out to when relevant opportunities arise. This will also provide you with access to future projects available through our expert network.

### Key Responsibilities
- Quality monitoring: Spot-check biomedical engineering items, identify quality issues, provide ongoing feedback through DMs, and escalate recurring or critical issues.
- Technical review: 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.
- Trainer and QA communication: Update trainers and QAs on Discord about new item guidelines, project changes, workflow updates, quality expectations, and biomedical-engineering-specific review standards.
- Question handling: 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.
- 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 biomedical 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 biomedical-engineering-specific review requirements.
- Quality alignment: Ensure all trainers and QAs apply biomedical engineering guidelines consistently and understand updates as projects evolve.
- Risk and safety review: 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.
- Process improvement: Identify recurring quality gaps, propose workflow improvements, and help build scalable QA processes for biomedical engineering AI training projects.

### Your Profile
- 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.