Software Engineer, Systems

Meta is seeking a Staff Systems Software Engineer to design and build the foundational infrastructure that powers products used by billions of people worldwide. In this role, you will architect and implement large-scale distributed systems, low-level platform components, and high-performance services that underpin Meta’s core product stack. You will drive technical strategy across system reliability, performance, and scalability, partnering closely with product, infrastructure, and data engineering teams to deliver systems that operate at global scale with high availability and efficiency.

• 8+ years of experience designing and implementing large-scale distributed systems, platform infrastructure, or systems software in production environments
• Experience leading major technical initiatives end-to-end, including architecture design, cross-team coordination, staged rollout, and post-launch reliability ownership
• Experience debugging complex, non-reproducible systems issues including concurrency bugs, memory management failures, and distributed consistency problems
• Experience defining service level objectives, building observability infrastructure, and driving reliability improvements across production systems
• Experience communicating technical architecture decisions and trade-offs in writing to both engineering and non-engineering stakeholders
• Experience with systems programming languages such as C, C++, or Rust in the context of high-performance or low-latency infrastructure
• Experience building or improving developer tooling, automation frameworks, or internal platforms that measurably improve engineering efficiency across teams
• 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
• Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
• Experience designing or contributing to storage systems, compute scheduling frameworks, or inter-service communication protocols at scale
• Demonstrated use of AI tools to accelerate systems design, automate operational workflows, or improve code quality and test coverage

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