Software Engineer, AI Specialist - Wearables AI (Technical Leadership)

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• Software Engineer, AI Specialist - Wearables AI (Technical Leadership)
• Burlingame, CA
• Artificial Intelligence
• + 2 more

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.

Software Engineer, AI Specialist - Wearables AI (Technical Leadership) 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

Minimum 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

Preferred Qualifications

• 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

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