Staff AI Researcher (Foundation AI)

Posted 6 Hours Ago
Be an Early Applicant
Boston, MA, USA
Hybrid
215K-260K Annually
Senior level
Fitness • Hardware • Healthtech • Sports • Wearables
Power your performance with 24/7 data
The Role
Lead research, development, and deployment of large-scale multimodal foundation models integrating wearable, physiological, language, and behavioral data. Design self-supervised and representation-learning methods, build distributed training pipelines, and collaborate on productionization, evaluation, and downstream applications. Shape technical architecture, mentor researchers, and promote ethical, transparent, privacy-preserving AI systems that improve member health and performance.
Summary Generated by Built In

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.

Skills Required

  • Master's degree, Ph.D., or equivalent professional experience in Computer Science, Machine Learning, Electrical Engineering, or a related field
  • 7+ years of experience in applied machine learning, AI research, or large-scale modeling
  • Track record of delivering production machine learning systems
  • Expertise in modern deep learning, including transformers or state space models
  • Experience with multimodal model training
  • Proficiency in Python
  • Proficiency with deep learning frameworks such as PyTorch or TensorFlow
  • Experience building and scaling large datasets and training large models in multi-node, multi-GPU distributed environments
  • Familiarity with data, model, and context parallelism
  • Applied experience with representation learning, self-supervised methods, and post-training for downstream applications
  • Experience with reinforcement learning for foundation-model post-training, including PPO, DPO, or GRPO
  • Familiarity with MLOps, model versioning, evaluation, machine learning CI/CD, and cloud-based computing
  • Excellent communication and cross-functional collaboration skills
  • Passion for improving human performance and healthspan through science and technology

What the Team is Saying

Josh
Manan Dedhia
Anahis

WHOOP Compensation & Benefits Highlights

  • Healthcare Strength — Health coverage is considered a strong part of the package, including medical, dental, and vision insurance. Feedback suggests the plans are solid and align with a health-focused culture.
  • Parental & Family Support — Paid parental leave is notably generous, with additional transition time for returning to work. This breadth stands out as a meaningful family support.
  • Wellbeing & Lifestyle Benefits — Wellness perks are extensive, including a wellness stipend, free membership/device with an extra to gift, daily meals at HQ, gym/recovery access, and a sleep-performance bonus. Feedback suggests these health-aligned perks add tangible value to total rewards.

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The Company
HQ: Boston, MA
500 Employees
Year Founded: 2012

What We Do

At WHOOP, we’re on a mission to unlock human performance. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives. Our wearable device and performance optimization platform has been adopted by many of the world's greatest athletes and consumers alike.

Why Work With Us

At WHOOP, we’re focused on building an inclusive and equitable team with a strong sense of belonging for everyone—increasing representation in every way as our team grows. We believe that our differences are our source of strength—so much so it’s one of our core values.


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WHOOP Offices

Hybrid Workspace

Employees engage in a combination of remote and on-site work.

Typical time on-site: 4 days a week
HQBoston, MA
Limerick, Limerick, V94 4D83 Ireland
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