Summary: Meta is seeking a Staff Systems Software Engineer to design and build the foundational software infrastructure that powers Meta's products at massive scale. In this role, you will architect and implement complex systems software - spanning areas such as operating systems interfaces, runtime environments, low-level networking, storage, or platform services - that enables reliability, performance, and scalability across Meta's infrastructure. You will serve as a technical leader who drives engineering excellence, shapes the systems architecture roadmap, and partners across engineering disciplines to deliver high-impact solutions. Required Skills: Software Engineer, Systems Responsibilities: 1. Architect and implement large-scale systems software components, including low-level platform services, runtime environments, or infrastructure frameworks that underpin Meta's product ecosystem 2. Lead the technical design of systems initiatives, evaluating trade-offs across performance, reliability, scalability, and maintainability to drive sound engineering decisions 3. Identify and resolve complex systems-level performance bottlenecks using profiling, instrumentation, and advanced debugging techniques including static analysis and trace-based diagnostics 4. Define and enforce service level objectives, build observability infrastructure including dashboards and alerting, and drive mean-time-to-mitigation improvements during production incidents 5. Establish and evolve coding standards, testing strategies, and rollout practices for systems software across the team, including automated resiliency and overload testing 6. Leverage AI-assisted development workflows to accelerate systems design, code generation, and cross-disciplinary analysis, applying sound judgment on when deep systems expertise is required 7. Collaborate with cross-functional partners across infrastructure, product engineering, and hardware teams to align systems architecture with broader platform requirements 8. Drive execution of multi-team systems initiatives by coordinating dependencies, managing phased rollouts and migrations, and proactively surfacing and mitigating technical risks 9. Mentor other engineers on systems design principles, debugging methodologies, and AI-augmented development practices, and contribute to onboarding and engineering programs 10. Communicate technical decisions, architectural trade-offs, and systems health metrics clearly in writing and presentations to both engineering and non-engineering stakeholders Minimum Qualifications: Minimum Qualifications: 11. 8 years of experience in systems software engineering, including work on operating systems, runtime environments, low-level networking, storage systems, or large-scale platform infrastructure 12. Experience leading the end-to-end technical design and delivery of major systems software initiatives, including architecture definition, cross-team coordination, and production rollout 13. Experience diagnosing and resolving complex systems-level issues such as memory management bugs, concurrency and synchronization errors, or latency regressions using advanced debugging and profiling tools 14. Experience building reliable, observable systems software with well-defined SLOs, automated testing, staged rollout strategies, and production monitoring 15. Experience communicating systems architecture decisions and engineering trade-offs in writing to technical and non-technical audiences Preferred Qualifications: Preferred Qualifications: 16. Experience owning systems software that spans multiple infrastructure layers, with demonstrated ability to reason about upstream and downstream component dependencies 17. Experience integrating AI-assisted tooling into systems development workflows, including code generation, anomaly detection, or automated root cause analysis 18. Experience with systems programming in C, C++, or Rust, including kernel interfaces, memory allocators, threading models, or inter-process communication mechanisms 19. Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews) 20. Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements) 21. Track record of driving performance optimization initiatives at the systems layer, including CPU, memory, I/O, or network throughput improvements measured against defined baselines 22. Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Industry: Internet
