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

About the role

WHOOP is seeking a Senior ML Research Engineer to join the Sensor Intelligence Group (SIG). This role will contribute to both member-facing and regulated health features, requiring a balance of strong rigor in Machine-learning and Deep-learning fundamentals, and clinical-data/regulatory awareness. You will tackle the complex challenge of extracting reliable insights from noisy sensor data and deploying robust algorithms on constrained edge and cloud environments, ultimately delivering meaningful and personalized metrics to millions of members.

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. Awareness of the mathematics behind the algorithms is valued.
• 4+ years of work/academic experience as a Machine-Learning/Deep-Learning researcher (2+ years post-PhD work experience for those with a PhD). The requirements may be relaxed for exceptional candidates.
• Experience developing or supporting regulated or high-risk ML systems (e.g., digital health, software as a medical device), 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 high-quality standards.
• Demonstrated ability to think innovatively and adapt to changing requirements while consistently producing high-quality reports within tight deadlines.

This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary.

Pay

The U.S. base salary range for this full-time position is $150,000 - $215,000. Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job-related skills, experience, performance, and relevant education or training. In addition to the base salary, the successful candidate will also receive benefits and a generous equity package.