Senior Machine Learning Engineer (Sensor Intelligence)

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.
• Monitor and ensure 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 the 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 either 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, including the mathematics behind the algorithms.
• 4+ years of work/academic experience as a Machine-Learning/Deep-Learning researcher (2+ years post-PhD for PhD holders).
• Experience developing or supporting regulated or high-risk ML systems (e.g., digital health, software as a medical devices) is a plus.
• Strong experience with time series data (wearables, physiological signals, high-frequency sensor data) and signal processing.
• Strong experience with multiple DL architectures; experience training/fine-tuning/deploying Foundation AI models is a plus.
• Proficiency in Python (scientific stack), ML/DL frameworks (PyTorch, TensorFlow).
• Experience with cloud computing platforms (AWS or GCP) is a plus.
• Strong communication and collaboration skills across cross-functional teams.
• Commitment to leveraging AI tools while maintaining high-quality standards.