Job Description

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.

Responsibilities
Architect and implement large-scale systems software components, including low-level platform services, runtime environments, or infrastructure frameworks that underpin Meta's product ecosystem
* Lead the technical design of systems initiatives, evaluating trade-offs across performance, reliability, scalability, and maintainability to drive sound engineering decisions
* Identify and resolve complex systems-level performance bottlenecks using profiling, instrumentation, and advanced debugging techniques including static analysis and trace-based diagnostics
* Define and enforce service level objectives, build observability infrastructure including dashboards and alerting, and drive mean-time-to-mitigation improvements during production incidents
* Establish and evolve coding standards, testing strategies, and rollout practices for systems software across the team, including automated resiliency and overload testing
* 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
* Collaborate with cross-functional partners across infrastructure, product engineering, and hardware teams to align systems architecture with broader platform requirements
* Drive execution of multi-team systems initiatives by coordinating dependencies, managing phased rollouts and migrations, and proactively surfacing and mitigating technical risks
* Mentor other engineers on systems design principles, debugging methodologies, and AI-augmented development practices, and contribute to onboarding and engineering programs
* Communicate technical decisions, architectural trade-offs, and systems health metrics clearly in writing and presentations to both engineering and non-engineering stakeholders

Qualifications
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
* Experience leading the end-to-end technical design and delivery of major systems software initiatives, including architecture definition, cross-team coordination, and production rollout
* 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
* Experience building reliable, observable systems software with well-defined SLOs, automated testing, staged rollout strategies, and production monitoring
* Experience communicating systems architecture decisions and engineering trade-offs in writing to technical and non-technical audiences Experience owning systems software that spans multiple infrastructure layers, with demonstrated ability to reason about upstream and downstream component dependencies
* Experience integrating AI-assisted tooling into systems development workflows, including code generation, anomaly detection, or automated root cause analysis
* Experience with systems programming in C, C++, or Rust, including kernel interfaces, memory allocators, threading models, or inter-process communication mechanisms
* Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
* Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
* Track record of driving performance optimization initiatives at the systems layer, including CPU, memory, I/O, or network throughput improvements measured against defined baselines
* Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies