# Senior AI Researcher (Foundation AI)

**Company:** [WHOOP](https://hotfix.jobs/companies/whoop)
**Location:** Boston, MA
**Role:** AI Research
**Experience:** 7+ years
**Skills:** Python, PyTorch, TensorFlow, Transformers, Multimodal Learning, Self-Supervised Learning, Representation Learning, Distributed Training, MLOps
**Posted:** 2026-05-22

> Senior individual contributor building and deploying large-scale multimodal foundation models that integrate wearable sensor, biomarker, and behavioral data for personalized health insights.

## Job Description

## 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 multi-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.

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