Machine Learning Engineer

This role is ideal for an engineer who enjoys moving quickly from idea to prototype to product, creatively overcoming data limitations, and applying new tools to multi-modal sensor fusion problems in health and wellness. You will work across the full algorithm lifecycle including data strategy, modeling, evaluation, optimization, and deployment. 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 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 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

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