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Biomedical Data Science Engineer - Health Technologies

Apple - San Diego, United States of America

Posted May 6, 2026

Benefits

Parental leave
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Family-building benefits
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401(k) match
Listed Source: EMPLR_CONTRIB_INCOME_AMT. source Last checked Jun 13, 2026.

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About this role

Biomedical Data Science Engineer - Health Technologies San Diego, United States of America The Health Technologies Team conceives and proves out exciting new technology for Apple's future products and features. In this role, you can be involved in the end-to-end new product development cycle for health and wellness technologies, from product ideation and feasibility assessment to algorithm implementation and product validation. Join our team to create extraordinary features that represent the cutting edge of innovation in the dynamic consumer health field. In this role, you'll collaborate with multidisciplinary teams to create innovative health products and features. As a biomedical data scientist, your responsibilities will include: -design and support of lab and human studies ranging from small scale pilot investigations to large scale sensor fusion studies -distill and interpret study findings to assess system performance and confounders -define and evaluate sensor feasibility criteria, including KPI development, mapping user experience requirements to sensor specifications -develop analysis tools to evaluate and interpret physiological time series sensor data -design, implement, and validate physiological models and algorithms -develop data visualization strategies for sharing complex data and study findings to influence project decisions and direction. The role requires effective collaboration with team members spanning a broad range of expertise and disciplines. Flexible thinking, adaptability to change, comfort with ambiguity, and ability to work both independently as well as in a team setting are hallmarks of success on our team. Minimum Qualifications: MS in Biomedical engineering or other engineering discipline with relevant prior experience with time-series physiological

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