Top AI & Machine Learning Jobs in Boston, MA

Leads applied machine learning and statistical research for health features using longitudinal wearable, physiological, behavioral, and multimodal data. Designs studies, models, evaluation protocols, and quality standards; partners with engineers to productionize methods; advises product, clinical, regulatory, and digital health teams; mentors scientists and engineers; and communicates evidence, limitations, risks, and trade-offs to technical and executive stakeholders.

Develop production-ready machine learning and signal-processing algorithms for wearable physiological sensor data. Responsibilities include designing and evaluating deep learning models, analyzing large datasets, optimizing algorithms for embedded edge deployment, and balancing accuracy, power, memory, latency, and scalability. The role owns projects from research and prototyping through validation, deployment, monitoring, and continuous improvement while collaborating with scientists, engineers, hardware experts, and product teams.

Lead the technical direction and execution of WHOOP’s foundation AI modeling efforts across wearable sensor data, language, biomarkers, and behavioral signals. Drive high-risk research from hypothesis to production, shape AI strategy and infrastructure decisions, collaborate across research, product, and engineering, mentor senior technical talent, establish evaluation standards, and represent WHOOP externally through recruiting, partnerships, and the machine learning research community.

Leads WHOOP’s cloud-based machine learning organization responsible for sleep, recovery, and training algorithms. Manages ML scientists and engineers, establishes technical standards and development practices, guides architecture and deployment, and partners with platform, hardware, sensor, product, research, and engineering teams. The role requires scaling production ML systems, improving model validation, monitoring, and deployment, and translating physiological sensor capabilities into reliable consumer experiences.

Lead research, development, and deployment of large-scale multimodal foundation models integrating wearable, language, biomarker, clinical, and behavioral data. Develop self-supervised and representation-learning methods, distributed training pipelines, and post-training techniques. Collaborate with engineering, MLOps, data, product, and research teams to operationalize robust, observable, privacy-preserving models and translate capabilities into member-facing features. Contribute to technical roadmaps and architectural decisions for WHOOP’s foundation AI systems.

Design, build, and productionize scalable machine learning systems for personalized health and fitness metrics using physiological and behavioral data. Responsibilities include improving ML pipelines, translating research prototypes into reliable production services, developing operational infrastructure, implementing observability and incident response, and mentoring engineers and applied scientists.