Senior AI/ML Researcher (Foundation AI)

About the Role

WHOOP is seeking a Senior AI Researcher to join the Foundation AI team. This team builds multimodal foundation models integrating 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, pushing the boundaries of deep learning and ensuring 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 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) and 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.