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.

Responsibilities

• Manage teams of engineers and technical leaders who deliver large-scale infrastructure with significant cross-org dependencies
• Shape the technical strategy and roadmap for the productivity and systems infrastructure powering our wearables portfolio
• Stay hands-on, evaluating architecture, reviewing system designs, and contributing directly with AI-assisted tools
• Partner with product, hardware, QA, and data science to keep teams engaged substantively, not siloed
• Proactively surface and resolve execution risks across vision, strategy, and people to keep teams from being unblocked
• Foster strong engineering craftsmanship through investment in AI workflows and tooling
• Monitor organization health through regular feedback loops, pulse data, and structured development conversations

Minimum qualifications

• 8+ years of software engineering experience in systems, infrastructure, distributed systems, or related technical domains
• 5+ years of engineering management experience, including managing teams delivering large-scale systems projects operating at high scale and reliability
• 2+ 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

Preferred qualifications

• 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