Whoop5mo ago

Staff Machine Learning Engineer (Foundation AI)

Bostonlead

Data ScienceOtherMachine Learning EngineerStaff Machine Learning EngineerData & AI

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WHOOP is an advanced health and fitness wearable on a mission to unlock human performance and extend healthspan. By providing members with a deep understanding of their bodies, behaviors,

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Data ScienceOtherMachine Learning EngineerStaff Machine Learning EngineerData & AI

WHOOP is an advanced health and fitness wearable on a mission to unlock human performance and extend healthspan. By providing members with a deep understanding of their bodies, behaviors, and daily lives, WHOOP empowers healthier choices and peak performance.

We are seeking a Staff Machine Learning Engineer 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.

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.

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) and 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 distributed compute environments.

Strong applied experience with representation learning, self-supervised methods, and fine-tuning for downstream applications.

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.