Health Sensing ML Engineer

What you'll do

• Develop and implement machine learning and deep learning models using health sensing data.
• Analyze large-scale wearable sensor data to extract significant insights from time-series and biomedical signals.
• Manage the full ML development cycle, including setting up data pipelines and model evaluation.
• Perform in-depth failure analysis to identify model weaknesses and drive design decisions.
• Build end-to-end pipelines that support rapid iteration and reliability for multi-year projects.
• Execute large machine learning training jobs on distributed backends with high volumes of data.
• Translate algorithms into real-world applications through collaboration with clinical authorities 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 in the field.
• Proven experience developing machine learning and deep learning models, preferably in the health domain.
• 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.
• Understanding of data preprocessing, feature extraction, and model evaluation techniques.
• Familiarity with software development standard methods and teamwork.
• Ability to run large ML training jobs efficiently on a distributed backend with high volumes of data.