Staff AI Researcher (Foundation AI)

Whoop; Boston (MA); On-site; USD 180,000 - 240,000; Full time

Job summary

WHOOP is seeking a Staff AI/ML Researcher in Boston to lead the design, training, and deployment of large-scale multimodal foundation models that fuse wearable sensor data, text, and biomarkers to advance member health insights.

You will work closely with data scientists, ML engineers, and product teams across the company, mentor junior researchers, and help define the technical roadmap while ensuring ethical, privacy-preserving AI practices. relocation encouraged.

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).
• Experience building and scaling large datasets and training large models in 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.

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.
• Serve as a technical mentor for other data scientists, sharing best practices in deep learning, large-scale training, and multimodal data integration.
• Ensure models adhere to WHOOP’s standards for ethical, transparent, and privacy-preserving AI.

Skills

Transformers

State space models

Multimodal training

Python

PyTorch

TensorFlow

Distributed training

Data/model/context parallelism

Representation learning

Self-supervised learning

Reinforcement learning

MLOps

CI/CD for ML

Cloud compute

Communication

Education

Master's or PhD in CS/ML/EE

Tools

PyTorch

TensorFlow

Job description

At WHOOP, we’re on a mission to unlock and inspire performance for life. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. Our wearable technology collects rich physiological data, providing members with actionable insights into their recovery, training, and sleep.

We are seeking a Staff AI/ML 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 staff 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.

• Serve as a technical mentor for other data scientists, sharing best practices in deep learning, large-scale training, and multimodal data integration.

• 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).

• Experience building and scaling large datasets and training large models in 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.

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. An employer who violates this law shall be subject to criminal penalties and civil liability.