Senior Machine Learning Scientist (Sensor Intelligence) at WHOOP
Senior Machine Learning Scientist (Sensor Intelligence)
WHOOP
On-site
Boston, MA
Full-time
Salary not listed
Posted 22 April 2026
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Job Description
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 Scientist 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 machine learning systems that power the most foundational health features at WHOOP. You’ll enable next-generation AI coaching at Whoop, developing robust algorithms for constrained edge and cloud environments,ultimately delivering meaningful and personalized coaching to millions of members.
RESPONSIBILITIES:
- Research, architect, and develop ML systems for member coaching distributed between edge hardware and the cloud
- Collaborate with machine learning and edge ML engineers to translate prototypes into production
- Partner with product and user experience teams to ensure consistent user experience in bandwidth-constrained environments and to align with member impact and health insights goals
- Contribute to architectural decisions and mentor team members in ML best practices
QUALIFICATIONS:
- Bachelor's degree in Computer Science, Electrical/Computer Engineering, Applied Mathematics, or a related field; Master’s or PhD degree preferred
- 5+ years of experience as a Machine Learning Scientist or similar role with a focus on applied research, preferably related to voice and/or text-based conversational systems
- Experience training, fine-tuning, and deploying state-of-the-art deep learning architectures to production
- Experience with time-series foundation models and self-supervised training approaches
- Experience pre-training and fine-tuning small language models and/or building natural language understanding (NLU) models than run on resource-constrained targets
- Experience with cloud platforms (AWS or GCP) and familiarity with modern MLOps practices such as CI/CD, model versioning, monitoring, and observability
- Strong communication 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