The Health Sensing team builds outstanding technologies to support our users in living their healthiest, 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.

In this role, you will be at the forefront of developing ML algorithms for health sensing applications and ensuring the efficient evaluation of these models to be in production at scale. You will be interacting closely with ML engineers, clinicians, software and hardware engineers. You will be delivering solutions on time and with high quality standing up to the standards considering a customer facing product.

Proven experience in developing machine learning and deep learning models, preferably in the health domainStrong proficiency in Python and ML frameworks e.g. PyTorch, TensorflowExperience with health data analysis, including time-series data, sensor data, and biomedical signal processingSolid understanding of data preprocessing, feature extraction, and model evaluation techniquesFamiliar with software development standard methods/collaborationsSufficient SW skills to run large ML training jobs efficiently on a distributed backend with large volume of dataExcellent interpersonal skills; comfortable in a collaborative and ground breaking research environments