Senior Machine Learning Research Engineer (Deep Learning, Sensor Intelligence Group)

Responsibilities

• Design and train deep-learning (DL) and machine-learning (ML) models to extract valuable insights from large repositories of time-series/biosensor data.
• Stay up to date with the latest advancements in DL research and technologies.
• Support documentation of the algorithms for regulated health features.
• Write clean, efficient, and maintainable code.
• Maintain and monitor the proper functioning of algorithms across our diverse user population, addressing any issues related to data and data quality.
• Conduct experiments and perform rigorous testing of the models.
• Optimize and fine-tune DL/ML (including Foundation AI models) models for deployment in production systems, considering factors such as computational resources and real-time constraints.
• Prepare comprehensive reports for cross-functional teams.
• Contribute to ongoing research efforts and explore new features for the Whoop product.
• Collaborate with engineers from SIG, Data Science and Firmware teams to translate research prototypes into scalable, efficient, and cost-effective ML inference systems.

Qualifications

• Master’s or PhD degree in Computer Science, Electrical Engineering, Biomedical Engineering, Data Science, Artificial Intelligence, Statistics, or a related field.
• Must have published research papers in ML/DL domains, preferably application of ML/DL on biomedical data.
• Solid understanding of ML fundamentals, and particularly DL techniques.
• Experience developing or supporting regulated or high-risk ML systems (e.g., digital health, software as a medical devices), including familiarity with validation, documentation, and change-management requirements in regulated environments is a significant plus.
• Strong experience with time series data, e.g., data pertaining to wearables, physiological signals or any high-frequency sensor data.
• Familiarity with signal processing concepts and techniques is expected.
• Strong experience with multiple DL architectures is expected.
• Experience in training/fine-tuning/deploying Foundation AI models is a plus.
• Proficiency in Python (scientific stack), ML/DL frameworks and libraries, e.g., PyTorch, TensorFlow.
• Experience with cloud computing platforms (e.g., AWS or GCP) is a plus.
• Strong communication (both written and oral) and collaboration skills across cross-functional teams.
• Strong commitment to embracing and leveraging AI tools in day-to-day tasks, ensuring AI-assisted work aligns with the same high-quality standards as personal contributions.
• Demonstrated ability to think innovatively and adapt to changing requirements while consistently producing high-quality reports within tight deadlines.