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Whoop

Posted 7 months ago

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Staff Machine Learning Engineer (Foundation AI)

BostonOn-siteFull-time

AI Summary

Senior individual contributor who designs, trains, and deploys large-scale multimodal foundation models, collaborating with data scientists and engineers to deliver production-ready AI capabilities for WHOOP members.

About this role

WHOOP is an advanced health and fitness wearable on a mission to unlock human performance and extend healthspan. By providing members with a deep understanding of their bodies, behaviors, and daily lives, WHOOP empowers healthier choices and peak performance.
We are seeking a Staff Machine Learning Engineer to join our Foundation AI team. This team builds the multimodal foundation models that underpin WHOOP’s next generation of intelligent, personalized, and health-enhancing experiences. These models integrate data across wearable sensors, language, biomarkers, clinical information, and self-reported inputs to create scalable AI systems that understand human physiology and behavior.
In this role, you’ll serve as a senior individual contributor driving the research, development, and deployment of large-scale multimodal models. You’ll collaborate closely with data scientists, ML engineers, and cross-functional partners to push the boundaries of deep learning and ensure our models deliver measurable value to WHOOP members.

RESPONSIBILITIES:

  • Design, train, and optimize large-scale multimodal foundation models that integrate wearable sensor data, text, biomarkers, and behavioral data.
  • Conduct applied research in self-supervised learning, representation learning, and downstream task fine tuning to advance WHOOP’s core model capabilities.
  • Develop scalable, distributed training pipelines for large models on high-performance compute environments.
  • Collaborate with MLOps, data engineering, and software engineering teams to operationalize models for production deployment, ensuring robustness, reproducibility, and observability.
  • Partner with product and research teams to translate foundation model capabilities into downstream features that deliver meaningful member value.
  • Contribute to the technical roadmap and architectural direction for foundation model development at WHOOP.
  • Serve as a technical mentor for other data scientists, sharing best practices in deep learning, large-scale training, and multimodal data integration.
  • Ensure models adhere to WHOOP’s standards for ethical, transparent, and privacy-preserving AI.

    • QUALIFICATIONS:

  • Advanced degree (Master’s or Ph.D.) in Computer Science, Machine Learning, Electrical Engineering, or a related field, or equivalent professional experience.
  • 7+ years of experience in applied ML, AI research, or large-scale modeling, with a track record of delivering production systems.
  • Expertise in modern deep learning (e.g., transformers, state space models) and multimodal model training.
  • Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow).
  • Experience building and scaling large datasets and training large models in distributed compute environments.
  • Strong applied experience with representation learning, self-supervised methods, and fine-tuning for downstream applications.
  • Familiarity with MLOps best practices including model versioning, evaluation, CI/CD for ML, and cloud-based compute.
  • Excellent communication skills and ability to collaborate cross-functionally with engineers, researchers, and product teams.
  • Passion for WHOOP’s mission to improve human performance and extend healthspan through science and technology.

    • Skills

    Biomarkers Data IntegrationCI/CD For MLCloud ComputeData PipelinesDistributed TrainingETLLanguage ModelsLarge-scale Model TrainingMLOpsModel DeploymentModel VersioningMultimodal ModelingObservabilityPythonPyTorchRepresentation LearningSelf-supervised LearningTensorFlowText ProcessingTransformersWearable Sensor Data Integration

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