Software Engineering Manager, Wearables Infra

Meta Reality Labs is seeking a Software Engineering Manager to lead the productivity and systems infrastructure behind Meta's fastest-growing product category: AI-powered wearables. On the heels of Ray-Ban Meta AI glasses becoming one of the most popular consumer devices in Meta's history, we are scaling to a broad next-generation portfolio of smart glasses, wrist wearables, and augmented reality devices. You will manage teams building the large-scale, high-reliability infrastructure that keeps 2,000+ wearable developers fast and unblocked -- mainline health, build and release, test automation, on-demand devices and emulators, and developer tooling -- while driving technical strategy, championing AI-assisted engineering workflows, and partnering across product, QA, and data science to ship systems that are fast, resilient, and built to scale.

• 8+ years of software engineering experience in systems, infrastructure, distributed systems, or related technical domains5+ years of engineering management experience, including managing teams delivering large-scale systems projects operating at high scale and reliability2+ years of experience in managing other engineering managers or technical leaders
• Experience driving technical strategy and roadmap decisions for complex, high-scale systems with multiple layers of dependency
• Experience building and operating systems for reliability, availability, and performance -- setting and holding quality bars (SLAs/SLOs) as scope and load grow
• Experience scaling infrastructure, teams, and processes to keep pace with rapidly growing product and developer demand
• Experience in recruiting, developing, and retaining engineering talent, including managing performance and building team health
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience partnering with product and data science organizations to co-define systems requirements and deliver infrastructure that directly enables product outcomes
• Track record of integrating AI tools and workflows into engineering team practices to measurably improve delivery capacity and code quality
• 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
• Demonstrated ability to build and scale engineering organizations through periods of significant technical or organizational ambiguity