About the job
Apple’s Health Sensing team is seeking a versatile Machine Learning Engineer to develop next-generation health algorithms that deliver meaningful insights to users by combining classical ML, signal processing, and emerging generative AI techniques. Our team has delivered impactful features including heart rate notifications, ECG, blood oxygen, sleep apnea notifications, and overnight vitals to millions of Apple Watch users.
Responsibilities
Develop and validate ML and GenAI-driven algorithms for health sensing applications from concept through productization
Prototype and compare multiple approaches using real and synthetic data to accelerate algorithm development
Design experiments and evaluation methodologies to quantify performance and guide algorithm improvements
Optimize algorithms for robustness, efficiency, and on-device deployment constraints
Work cross-functionally with user studies, hardware, software, and product teams to bring algorithms into product
Analyze failure modes, quantify tradeoffs, and drive data-driven algorithm improvements
Qualifications
Minimum
Bachelors degree in Computer Science, Electrical Engineering, Biomedical Engineering, Statistics, Applied Mathematics, or related field, or equivalent industry experience.
Strong foundation in machine learning, statistics, signal processing, or applied mathematics for real-world sensing problems
Experience applying modern AI techniques, including generative AI and agentic AI, to accelerate algorithm development, data generation, and performance evaluation
Proficiency in Python for algorithm development and optimization
Demonstrated ability to rapidly prototype, evaluate multiple approaches, and iterate based on experimental results
Experience owning algorithm development from early exploration through validation and integration
Preferred
Experience developing algorithms for physiological sensing using multi-modal data
Familiarity with on-device ML frameworks or resource-constrained optimization
Experience working with incomplete, noisy, or limited datasets
Background in experimental design and statistical validation
Experience with distributed or cloud-based ML workflows
Experience accelerating development through simulation, synthetic data, or creative data augmentation approaches
Self-driven, curious engineer comfortable taking ambiguous sensing problems from concept to working solutions