Meta is seeking a distinguished software engineer with deep AI specialization to drive transformative technical initiatives for Wearables AI. In this role, you will define and lead the architectural direction of large-scale AI systems powering Meta's wearable devices — including smart glasses and next-generation wearable platforms. You will build intelligent on-device and cloud-based AI experiences spanning multimodal understanding, contextual assistants, and real-time interactive AI systems. This is a role for a technical leader who operates at the intersection of cutting-edge AI research and production-scale engineering, shaping both the systems and the culture that powers Meta's wearables AI future.
Responsibilities
Identify and solve the most complex AI modeling and systems challenges for wearables, including architecting an omni LLM for wearables interactions, optimized for power, latency, and compute constraints Define extensible technical foundations and cross-organizational standards for wearables AI model development, evaluation, and deployment pipelines across Meta's wearable device portfolio Drive the technical vision and multi-year roadmap for Wearables AI platform capabilities, influencing priorities across teams and cross-functional partners including research, hardware, product, and data science Evaluate emerging AI architectures and industry developments in wearables AI to identify opportunities and risks relevant to Meta's competitive position Lead the design and implementation of multimodal AI systems for wearables, including vision, audio, and agentic capabilities, reliability, and real-time performance are critical Identify where AI tooling and automation can eliminate entire categories of engineering work, and drive adoption of AI-native workflows across wearables engineering teams Collaborate with research scientists to translate novel AI techniques into production wearables systems that deliver seamless, intelligent user experiences Mentor engineers across the organization by providing customized technical coaching, leading architecture reviews, and establishing a culture of rigor for wearables AI development Partner with hardware, legal, policy, and compliance teams to ensure wearables AI systems meet privacy, security, and integrity standards for always-on, sensor-rich devices Define new metrics and data-driven decision-making principles for long-term wearables AI initiatives, connecting technical outcomes to organization-level priorities and business impact
Qualifications
Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 12+ years of experience in software engineering with a focus on AI, LLM systems, or applied AI in production environments Experience architecting and delivering large-scale AI, including training infrastructure, model serving, or foundation model pipelines Experience leading multi-team technical initiatives end-to-end, including defining strategy, driving cross-functional alignment, and delivering measurable outcomes against organization-level goals Experience identifying and resolving systemic engineering issues that span models, multiple systems or abstraction layers, including developing frameworks that prevent recurring classes of failures Experience communicating complex AI designs and technical trade-offs in writing and presentations to both technical and non-technical audiences, including engineering leadership Contributions to peer-reviewed AI research (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, KDD) or demonstrated track record of translating research advances into production AI systems Experience with large-scale Omni LLM training optimization, distributed training frameworks, or inference efficiency techniques such as quantization, distillation, or speculative decoding Experience with conversational AI, vision understanding, wearables AI systems Experience applying AI and automation tooling to eliminate categories of engineering toil and measurably improve team-level or organization-level engineering efficiency