Whoop - Senior AI/ML Researcher (Foundation AI)
Requirements
• 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.
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.
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