Health Sensing ML Engineer

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.

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 interact closely with ML engineers, clinicians, software and hardware engineers. You will deliver solutions on time and with high quality standing up to the standards of a customer facing product.

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

Interpersonal skills; comfortable in a collaborative and ground breaking research environments MS or PhD or equivalent experience