# Principal AI/ML Researcher

**Company:** [WHOOP](https://hotfix.jobs/companies/whoop)
**Location:** Boston, MA
**Role:** AI Research
**Experience:** 7+ years
**Skills:** large-scale machine learning, ai research, Transformers, state space models, multimodal training, self-supervised learning, representation learning, RLHF, ppo, dpo, grpo, Distributed Training, data parallelism, model parallelism, context parallelism
**Posted:** 2026-07-17

> 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.

## Job Description

## 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.

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