Principal AI/ML Researcher

About the role

You will define and lead the technical vision for WHOOP's Foundation AI team, shaping how large-scale models turn multimodal data into actionable health insights. This role owns the end-to-end execution of core modeling workstreams, from research hypothesis to productionized systems that power WHOOP's next generation of intelligent experiences. You will make the highest-stakes architectural decisions that balance scientific rigor with real-world constraints on wearable devices. You will set the standard for technical excellence, ensuring models are robust, interpretable, and aligned with user safety and privacy. You will act as a force multiplier, raising the technical bar across the team through mentorship, design reviews, and rigorous evaluation practices. You will represent WHOOP externally, building credibility in the research community and strengthening partnerships that advance the state of health AI. Above all, you will drive progress toward WHOOP's mission to improve human performance and extend healthspan through disciplined, science-first technology.

Key facts

What you'll do

Define the architectural direction for large-scale models that integrate wearable sensor streams, text, biomarkers, and behavioral signals, and personally own the most consequential, hard-to-reverse design choices.

Identify high-risk research bets in self-supervised and representation learning, shepherd them from speculative thesis to validated production capability, and decide when to pivot or discontinue unpromising directions.

Shape the broader AI strategy and standards at WHOOP, influencing platform, compute, and infrastructure choices so the team operates from a strong, scalable foundation.

Partner closely with product and engineering to translate technical breakthroughs into measurable product and business outcomes, ensuring research impact is realized in real member experiences.

Elevate the technical caliber of the organization by growing staff and senior engineers, strengthening design-review norms, and multiplying your contribution through mentorship and cross-team collaboration.

Serve as a visible technical authority who raises the bar across the organization, setting evaluation rigor, safety standards, and best practices for model development.

Represent WHOOP in the wider AI research community, contributing to recruiting, strategic partnerships, and visibility through talks, publications, and community engagement.

Champion the use of multimodal training, self-supervised representation learning, and advanced post-training methods such as RLHF to align models with human physiology and behavior.

Ensure that models are built with awareness of deployment constraints on wearable hardware, balancing performance, efficiency, and robustness in real-world conditions.

Drive the creation of scalable data pipelines and evaluation frameworks that continuously validate model performance and inform research priorities.

Lead efforts to operationalize foundation models within WHOOP's product stack, coordinating with engineering to deliver reliable, high-performance AI experiences.

Establish clear evaluation protocols and guardrails that keep model outputs aligned with health and safety standards, and iterate based on empirical evidence.

Foster a culture of scientific curiosity and rigor, encouraging the team to pursue ambitious questions while maintaining disciplined execution and measurable outcomes.

Act as a bridge between cutting-edge research and deployed health technology, ensuring that theoretical advances translate into durable, user-centric capabilities.

Requirements

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 through mentorship and process improvement.

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.

Nice to have

None specified.

Practical notes

This role is based in the WHOOP office located in Boston, MA. The successful candidate must be prepared to relocate if necessary to work out of the Boston, MA office.

WHOOP is an Equal Opportunity Employer and participates in E-verify to determine employment eligibility. It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment.