About the job
The Health Sensing team builds outstanding technologies to support our users in living their healthiest, the happiest lives by providing them with objective, accurate, and timely information about their health and well-being. As part of the larger Sensor SW & Prototyping team, we develop algorithms for a variety of health sensors, including PPG, accelerometer, ECG.
Responsibilities
Develop and implement machine learning and deep learning models using health sensing data
Analyze large-scale health data from wearable sensors to extract significant insights
Work across the entire ML development cycle, from setting up data pipelines to model evaluation
Analyze model behavior and finding weaknesses; drive design decisions with in-depth failure analysis
Build end-to-end pipelines that prioritize rapid iterations in support for reliability of a complex multi-year projects
Work multi-functionally to bring algorithms to real-world applications; this can span a wide range of partnerships with clinical authorities and engineering specialists across HW and SW
Qualifications
Minimum
BS in Computer Science, Engineering, Information Systems, or related technical field and a minimum of 3 years of equivalent experience
Proven experience in developing machine learning and deep learning models, preferably in the health domain
Proficiency in Python and ML frameworks e.g. PyTorch, Tensorflow
Experience with health data analysis, including time-series data, sensor data, and biomedical signal processing
Proven understanding of data preprocessing, feature extraction, and model evaluation techniques
Familiar with software development standard methods/teamworks
Sufficient SW skills to run large ML training jobs efficiently on a distributed backend with large volume of data
Preferred
Interpersonal skills; comfortable in a collaborative and ground breaking research environments
MS or PhD or equivalent experience