The Health Sensing team at Apple is looking to develop and implement ML algorithms for health sensing applications, ensuring efficient evaluation and production of these models at scale to provide users with objective, accurate, and timely health information.
Requirements
- Proven experience in developing machine learning and deep learning models, preferably in the health domain
- Strong 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
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
Other
- BS in Computer Science, Engineering, Information Systems, or related technical field and a minimum of 3 years of equivalent experience
- Excellent interpersonal skills; comfortable in a collaborative and ground breaking research environments
- MS or PhD or equivalent experience
