Health Sensing ML Engineer

Summary

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.

Minimum Qualifications

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 Qualifications

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

Description

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.

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