Health Algorithms Validation & Verification Engineer

The Applied Sensing & Health team has built innovative ways for users to improve their health, watch for their lives, and get personalized insights from their daily patterns through multimodal sensing and AI models. This team combines advanced machine learning, including integration, adaptation, and tuning of foundation models, with contextual sensing, bolstered by innovative study design, to derive user insights spanning health, fitness, and safety. This has enabled us to deliver meaningful features like Cardio Fitness, Journaling Suggestions, Fall and Crash Detection, and our latest advances to bring our suite of Fitness metrics to AirPods Pro 3, delivering 50 workout types in a single year.

We are a dynamic, collaborative group that works at the intersection of scientific research and product development. We develop science-backed designs of best-in-class AI/ML models that capture the multifactorial nature of human health and behavior, delivering insights straight to our customers. We promote innovation and new technology to further improve our creative output. We are seeking a tenacious and passionate person to join this amazing team. If you feel this is you, we'd love to hear from you.

Responsibilities:
In this role, you will be highly involved in driving design data quality and algorithm performance to develop our next generation of Health and Fitness features. This includes:

• Evaluating large user study datasets
• Design reviews
• Algorithm testing
• Bug reporting and triaging

Qualifications:
Familiarity building data pipelines for large-scale data processing.
Experience working with databases (SQL/NoSQL), comfortable with query languages and designing tables, views, indices, familiarity with AWS services/API such as S3 is a plus.
Familiarity testing hardware and/or testing software (including designing, implementing and executing test plans) is a plus.
Detail-oriented, able to find bugs, investigate & debug issues, and drive solutions to difficult problems.
Excellent analytical and problem solving skills, self-motivated, laser-focused, and solution-oriented.

Education:
BS, MS, or PhD in Computer Science / Electrical Engineering / Mechanical Engineering or equivalent.
Background in one or more of the following domains: time-series analysis, signal processing, sensor fusion, AI models, statistics, health & fitness.
Proficient at coding in Python and/or Matlab.
Proficient at data analysis, data visualization, and reporting.
Experience in validating AI models, crafting benchmarks and evaluation protocols, and performing statistical analysis.