Posted 1 month ago

Senior Machine Learning Scientist (Sensor Intelligence)

BostonOn-siteFull-time

AI Summary

Senior ML Scientist focusing on building and deploying ML systems for member coaching across edge and cloud environments, collaborating with cross-functional teams to scale AI features for WHOOP devices.

About this role

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

Skills

Cloud Platforms (AWS Or GCP)Deep LearningDistributed SystemsDistributed TrainingEdge MLLMS / LLM Fine-tuningMLOps (CI/CD, Model Versioning, Monitoring)Model EvaluationNLP / NLUProduction ML DeploymentPythonPyTorchResource-constrained InferenceSelf-supervised LearningTensorFlowTime-series ModelingVision (optional)