Principal AI/ML Researcher

About the Role

WHOOP is seeking a Principal AI Researcher to join our Foundation AI team. This team builds the 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 will be one of the senior technical leaders at WHOOP. You will own the technical execution of our foundation modeling workstreams and your technical judgment will shape a core part of WHOOP’s AI efforts and the products they power.

Responsibilities

• Set the architectural direction for the development of large-scale models spanning wearable sensor data, text, biomarkers, and behavioral signals, and personally own the highest-stakes, hardest-to-reverse design decisions.
• Identify which research bets in self-supervised and representation learning are worth making, and drive the most ambiguous and unproven ones from thesis to validated, production capability.
• Contribute to the broader AI strategy and standards at WHOOP, shaping the platform, compute, and infrastructure choices the team operates on.
• Partner across research, product, and engineering to connect technical breakthroughs to product and business outcomes.
• Raise the technical bar across the team: grow staff and senior engineers, strengthen design-review and evaluation norms, and act as a force multiplier.
• Represent WHOOP's technical work externally, helping with recruiting, partnerships, and visibility in the broader AI research community.

Qualifications

• Advanced degree (Master’s or Ph.D.) or equivalent depth, with extensive experience in large-scale machine learning and AI research.
• A track record of owning technical direction for a significant area and making architectural decisions whose impact outlived any single project.
• Demonstrated ability to take ambiguous, high-risk research directions all the way to production impact and the judgment to discontinue those that will not.
• Deep expertise in modern deep learning (transformers, state space models), multimodal training, self-supervised and representation learning, RL-based post-training (PPO/DPO/GRPO), and large-scale distributed training (data, model, and context parallelism).
• A history of being a technical authority that teams defer to, and evidence of multiplying their output.
• Excellent communication and the ability to influence both the engineering bench and senior leadership.
• Publications and evidence of community building at top-tier machine learning venues.
• Passion for WHOOP's mission to improve human performance and extend healthspan through science and technology.
• Based in or prepared to relocate to Boston, MA.