VP, Foundation AI

RESPONSIBILITIES:

• Lead a world-class team in the design, training, evaluation, and deployment of large-scale multimodal foundation models spanning wearable sensor data, language, blood biomarkers, clinical datasets, and self-reported inputs
• Serve as the senior technical authority on foundation model architecture, representation learning, and training strategy, guiding critical design and investment decisions
• Build, grow, and mentor a high-performing AI organization, fostering a culture of technical excellence, collaboration, accountability, and continuous learning
• Partner closely with MLOps, data engineering, and software engineering teams to scale and serve foundation models in high-throughput, production environments
• Define and drive WHOOP’s long-term AI strategy, ensuring alignment between foundational research, product innovation, and company goals in health, performance, and longevity
• Establish rigorous standards for model evaluation, validation, and monitoring, with a focus on robustness, generalization, and real-world performance
• Communicate technical vision, milestones, and tradeoffs clearly to executive leadership and cross-functional stakeholders to ensure alignment and organizational buy-in

QUALIFICATIONS:

• Deep expertise in modern AI and machine learning, demonstrated through significant professional or academic experience building large-scale learning systems deployed in real-world environments
• At least 10 years of experience in AI and machine learning, including a minimum of 5 years leading and scaling high-performing technical teams or organizations
• Proven hands-on experience developing large models from scratch using distributed training frameworks such as PyTorch or JAX, including ownership of data pipelines, training infrastructure, optimization strategies, and evaluation methodologies
• Direct experience designing or leading foundation models or similarly generalizable representation learning systems that support multiple downstream tasks or modalities
• Demonstrated ability to translate cutting-edge research into durable, user-facing products that deliver sustained and measurable real-world value
• Experience working with complex, high-dimensional, and noisy data sources, including time-series sensor data or multimodal datasets
• Strong judgment around model robustness, evaluation, and failure modes, with an understanding of how modeling decisions impact user trust, safety, and outcomes in high-stakes applications
• Experience partnering closely with product, engineering, and infrastructure teams to deliver AI systems that balance scientific ambition with scalability, performance, and maintainability
• Track record of operating effectively in regulated, safety-critical, or trust-sensitive domains, or of applying equivalent rigor in environments where correctness and reliability are essential
• Exceptional communication skills, with the ability to articulate technical vision, tradeoffs, and progress to executive leadership and to both technical and non-technical audiences
• A leadership style that combines high technical standards with empathy, clarity, and a strong commitment to developing inclusive teams and future technical leaders