Research Scientist Manager, AI for Wearables and Health

Meta is seeking a Research Scientist manager to lead teams advancing AI capabilities for wearables and health applications. In this role, you will manage research & engineering teams working on machine learning solutions for wearable devices, health monitoring, biometric sensing, and wellness-focused AI systems.

You will shape the research strategy for AI-powered health and wearables experiences, guide the translation of novel findings into impactful product features, and partner closely with hardware, applied research, engineering, and product organizations to deliver meaningful health and wellness outcomes at scale.

Responsibilities

• Manage multiple teams of research scientists and engineers working on AI for wearables and health, including post-training, sensor fusion, and biometric modeling
• Define and drive the research strategy and roadmap for AI-powered health and wearables experiences, setting goals informed by team insights, emerging health AI literature, and Meta's product priorities
• Maintain technical engagement across teams by evaluating research and product direction and quality, contributing directly to experimental design, model architecture decisions for on-device deployment, and publication strategy
• Partner cross-functionally with MSL, product, UXR, and data science teams to translate health and wearables AI advances into measurable improvements across Meta's devices
• Recruit, develop, and retain research scientists and research leaders with expertise in health AI, wearable computing, physiological signal processing, and on-device machine learning
• Proactively identify and resolve execution risks across research projects, including experimental bottlenecks, compute and power constraints for wearable devices, and alignment between research outputs and health product requirements
• Champion a team culture that values scientific rigor, reproducibility, responsible AI practices, and privacy-conscious approaches to health data
• Hold research leaders accountable for performance, scientific depth, and cross-functional engagement, providing consistent and constructive feedback
• Communicate research priorities, trade-offs, and strategic implications clearly to leadership, maintaining a perspective on key advances in health AI and wearable computing

Qualifications

• 8+ years of experience in machine learning research or applied machine learning, with depth in areas such as LLM mid/post-training, applied AI, wearable computing, or on-device machine learning
• 4+ years of experience managing research or engineering teams, including experience managing other research leaders or technical leads
• Experience driving research strategy and roadmap decisions across the full ML research lifecycle, from problem formulation and experimentation through publication and production impact
• Experience partnering cross-functionally with hardware, engineering, product, and data science teams to translate ML research into measurable outcomes for consumer devices
• Experience recruiting, developing, and retaining research scientists and building high-performing research teams Experience with on-device ML optimization, including model compression, efficient inference, and power-aware deployment for resource-constrained hardware
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience managing teams working on applications of large language models in consumer tech
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
• Track record of publishing impactful research at top ML venues such as NeurIPS, ICML, ICLR, CHIL, or equivalents, and guiding teams to do the same
• Experience adhering to and implementing responsible and ethical AI practices for health applications, including privacy protection, bias mitigation, and clinical validation considerations