Senior Machine Learning Engineer (Sensor Intelligence)

Whoop•Boston, MA

2d•$150,000 - $210,000•Onsite

About The Position

At WHOOP, we're on a mission to unlock human performance and healthspan. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. WHOOP is seeking a Senior Machine Learning Engineer to join the Sensor Intelligence Group (SIG), a cross-functional team collaborating across WHOOP Labs, Firmware, and Data Science. This role is central to developing and scaling core physiological signal processing and machine learning systems that power WHOOPâs most foundational health features. 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. This role will contribute to both member-facing and regulated health features, requiring a strong balance of ML rigor, production readiness, and regulatory awareness. Join us in pushing the boundaries of wearable technology and positively impacting people's lives!

Requirements

Bachelor's degree in either Computer Science, Electrical engineering, Biomedical engineering, Data Science, Artificial Intelligence, Statistics, or a related field; Masterâs or PhD degree preferred.
5+ years of work experience as a Machine Learning Engineer working on research and development problems (4+ years for those with Masterâs degree and 2+ years for those with PhD degree). The requirements may be relaxed for exceptional candidates.
Must have experience working with multiple DL architectures. Experience in training/fine-tuning/deploying Foundation AI models is a Significant Plus.
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.
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.
Proficiency in Python (scientific stack), ML/DL frameworks and libraries, e.g. PyTorch, TensorFlow.
Demonstrated success in designing and deploying ML inference systems at scale, including real-time and batch architectures is a plus. 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.

Nice To Haves

Experience in training/fine-tuning/deploying Foundation AI models is a Significant Plus.
Demonstrated success in designing and deploying ML inference systems at scale, including real-time and batch architectures is a plus. Experience with cloud computing platforms (e.g. AWS or GCP) is a plus.
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 plus.

Responsibilities

Design and train deep-learning (DL) and machine-learning (ML) models to extract valuable insights from large repositories of time-series/biosensor data.
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.
Write clean, efficient, and maintainable code that is production ready.
Stay up to date with the latest advancements in AI/DL research and technologies.
Monitor and ensure the proper functioning of algorithms across our diverse user population, addressing any issues related to data and data quality.
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.
Prepare comprehensive reports for cross-functional teams.
Own the full lifecycle of ML service(s) from development to deployment. Be ready to partner with data engineers to build and enhance data pipelines, validation tools, and monitoring systems that ensure consistent model performance in production.
Mentor team members in ML engineering best practices.
Develop, validate, and maintain ML algorithms for regulated health features, ensuring compliance with applicable regulatory and quality requirements.