Health Sensing ML Engineer

What you'll do

• 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.
• Manage the entire ML development cycle from data pipeline setup to model evaluation.
• Perform in-depth failure analysis to identify model weaknesses and drive design decisions.
• Build end-to-end pipelines to support rapid iterations and reliability for multi-year projects.
• Execute large ML training jobs efficiently on a distributed backend with high data volumes.
• Translate algorithms into real-world applications through partnerships with clinical and engineering specialists.

What we're looking for

• BS in Computer Science, Engineering, Information Systems, or a related technical field.
• Minimum of 3 years of equivalent experience.
• Proven experience developing machine learning and deep learning models, preferably in the health domain (preferred).
• Proficiency in Python and ML frameworks such as PyTorch or 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.
• Familiarity with software development standard methods and teamwork.
• Sufficient software skills to run large ML training jobs efficiently on a distributed backend with large volumes of data.
• MS or PhD or equivalent experience (preferred).