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WHOOPWHOOPBoston, MA

Principal AI/ML Researcher

Principal AI/ML Researcher leading foundation model development for wearable health data at WHOOP. Owns architectural direction, high-risk self-supervised and multimodal research from thesis to production, and raises the technical bar for the AI team. Requires PhD-level expertise, publications, and track record of impactful technical leadership.

Salary not listed
On-site7+ YOEAI Research

About the role

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, not just working within them.
  • 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.

Skills

large-scale machine learningai researchTransformersstate space modelsmultimodal trainingself-supervised learningrepresentation learningRLHFppodpogrpoDistributed Trainingdata parallelismmodel parallelismcontext parallelism

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