We are seeking a Senior AI Researcher to join our Foundation AI team. This team builds the multimodal 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'll serve as a senior individual contributor driving the research, development, and deployment of large-scale multimodal models. You'll collaborate closely with data scientists, ML engineers, and cross-functional partners to push the boundaries of deep learning and ensure our 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 WHOOP's 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), multimodal model training.
• Proficiency in Python and deep learning frameworks (e.g., PyTorch, TensorFlow).
• Familiarity with training models on mulit-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.

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.

Interested in the role, but don't meet every qualification? We encourage you to still apply! At WHOOP, we believe there is much more to a candidate than what is written on paper, and we value character as much as experience. As we continue to build a diverse and inclusive environment, we encourage anyone who is interested in this role to apply.

The WHOOP compensation philosophy is designed to attract, motivate, and retain exceptional talent by offering competitive base salaries, meaningful equity, and consistent pay practices that reflect our mission and core values.

At WHOOP, we view total compensation as the combination of base salary, equity, and benefits, with equity serving as a key differentiator that aligns our employees with the long-term success of the company and allows every member of our corporate team to own part of WHOOP and share in the company's long-term growth and success.

The U.S. base salary range for this full-time position is $190,000 - $230,000. Salary ranges are determined by role, level, and location. Within each range, individual pay is based on factors such as job-related skills, experience, performance, and relevant education or training.

In addition to the base salary, the successful candidate will also receive benefits and a generous equity package.

These ranges may be modified in the future to reflect evolving market conditions and organizational needs. While most offers will typically fall toward the starting point of the range, total compensation will depend on the candidate's specific qualifications, expertise, and alignment with the role's requirements.