Machine Learning Deep Learning Research Engineer Jobs

Job Summary:
WHOOP is a company focused on enhancing human performance and healthspan through technology. They are seeking a Senior Machine Learning Research Engineer to contribute to member-facing and regulated health features by developing algorithms that extract insights from sensor data and deploying them in edge and cloud environments.
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:
Required:
• 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. At the SIG team, we like to be aware of the mathematics behind the algorithms we use
• 4+ years of work/academic experience as a Machine-Learning/Deep-Learning researcher (2+ years, post-PhD work experience with those having a PhD degree). 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 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.
• This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.
Preferred:
• 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.
• Experience with cloud computing platforms (e.g. AWS or GCP) is a plus.
• Strong experience with multiple DL architectures is expected. Experience in training/fine-tuning/deploying Foundation AI models is a plus.
Company:
WHOOP provides wearable fitness technology and a subscription platform that tracks physiological data for health and performance insights. Founded in 2012, the company is headquartered in Boston, USA, with a team of 501-1000 employees. The company is currently Late Stage.