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.
Top Skills: Bayesian StatisticsFrequentist StatisticsHr/HrvImuLongitudinal Data AnalysisMachine LearningNumpyPandasPpgPythonPyTorchScikit-LearnScipyTensorFlowTime-Series Analysis
Develop and deploy signal-processing algorithms and machine-learning models that convert wearable sensor data into real-time physiological insights. Responsibilities include data analysis, model training, Python prototyping, optimized C/C++ implementation, embedded firmware integration, and production validation. The role requires collaboration with firmware, hardware, data science, and domain teams to optimize accuracy, power, memory, compute, and latency while conducting rigorous offline, laboratory, real-world, and on-device testing.
Top Skills: CC++Digital Signal ProcessingEmbedded SystemsFirmwareMachine LearningPythonPyTorchScikit-LearnTensorFlowWearable Sensors
Lead research, development, and deployment of large-scale multimodal foundation models integrating wearable, physiological, language, and behavioral data. Design self-supervised and representation-learning methods, build distributed training pipelines, and collaborate on productionization, evaluation, and downstream applications. Shape technical architecture, mentor researchers, and promote ethical, transparent, privacy-preserving AI systems that improve member health and performance.
Top Skills: Ci/CdCloud ComputingDistributed ComputingDpoGrpoMlopsMulti-Gpu ComputingPpoPythonPyTorchState Space ModelsTensorFlowTransformers
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.
Top Skills: CC++Deep LearningDigital Signal ProcessingEdge AiEmbedded SystemsMachine LearningPythonStatistical Signal ProcessingWearable Sensors
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.
Top Skills: AIBiomarkersClinical DataContext ParallelismData ParallelismDeep LearningDistributed TrainingDpoGrpoMachine LearningModel ParallelismMultimodal TrainingPpoReinforcement LearningRepresentation LearningSelf-Supervised LearningState Space ModelsTransformersWearable Sensor Data
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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.
Top Skills: AccelerometersBioimpedance SensorsMachine LearningMlopsPpg SensorsTemperature SensorsTime-Series Modeling
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.
Top Skills: Ci/CdCloud ComputingContext ParallelismData ParallelismDistributed ComputingDpoGrpoMlopsModel ParallelismMulti-Gpu ComputingMulti-Node ComputingMultimodal AiPpoPythonPyTorchReinforcement LearningRepresentation LearningSelf-Supervised LearningState Space ModelsTensorFlowTransformers
Leads and manages an AI Quality engineering team responsible for evaluating and improving AI outputs. Defines testing frameworks for model performance and drift, establishes technical strategy and architecture, provides technical direction, manages technology vendors and partnerships, drives engineering quality improvements, and mentors team members.
Top Skills: Ai/Ml Model TestingApplied AiMachine LearningModel Drift MonitoringSoftware Deployment Lifecycles
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.
Top Skills: APIsAWSCloud InfrastructureGCPMl Data PipelinesPython
WHOOP
Senior Machine Learning Research Engineer (Deep Learning, Sensor Intelligence Group)