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WHOOPWHOOPBoston, MA

Senior AI Researcher (Foundation AI)

Senior individual contributor building and deploying large-scale multimodal foundation models that integrate wearable sensor, biomarker, and behavioral data for personalized health insights.

Salary not listed
On-site7+ YOEAI Research

About the role

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.
  • 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), multimodal model training.
  • Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow).
  • Familiarity with training models on multi-node, multi-GPU distributed compute environments.
  • Familiarity with best practices for data, model, and context parallelisms.
  • Strong applied experience with representation learning, self-supervised methods, and post-training for downstream applications.
  • Experience with reinforcement learning for post-training foundation models (PPO, DPO, GRPO etc.).
  • 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

PythonPyTorchTensorFlowTransformersMultimodal LearningSelf-Supervised LearningRepresentation LearningDistributed TrainingMLOps

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