Software Engineer, Systems

Meta•Menlo Park, CA

1d•$121,992 - $181,000

About The Position

Meta is looking for a Software Engineer with a systems specialization to help build and scale the foundational infrastructure that powers Meta's family of products and services. In this role, you will design, develop, and maintain systems-level software that operates at massive scale, ensuring reliability, performance, and efficiency across critical infrastructure. You will work closely with cross-functional engineering teams to solve complex systems challenges, from low-level kernel and runtime optimization to distributed systems design, contributing directly to the infrastructure that billions of people depend on every day.

Requirements

Currently has, or is in the process of obtaining a Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience. Degree must be completed prior to joining Meta
2+ years of experience developing systems-level software in C, C++, Rust, or a comparable low-level language
Experience with operating systems internals, including process management, memory management, file systems, or networking subsystems in Linux or a comparable UNIX-based environment
Experience designing and implementing components within distributed systems, including considerations for fault tolerance, consistency, and scalability
Experience writing automated tests, debugging production issues using profiling and tracing tools, and building observability instrumentation for systems software
Experience collaborating with cross-functional engineering teams to deliver medium to large software features independently from design through production

Nice To Haves

Experience contributing to or maintaining open-source systems software projects
Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews)
Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies
Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements)
Exposure to applying AI-assisted development tools to accelerate systems engineering work, including reviewing and validating generated code for correctness and safety
Familiarity with container orchestration platforms, cluster management systems, or large-scale compute infrastructure
Experience with performance profiling and optimization at the systems level, including CPU, memory, I/O, and network analysis

Responsibilities

Design and implement systems-level software components, including low-level libraries, runtime environments, and infrastructure services that operate at large scale
Develop and optimize performance-critical code paths, identifying and resolving bottlenecks across compute, memory, storage, and network layers
Write automated tests covering expected behaviors, failure modes, and error paths to ensure reliability of systems software
Instrument systems with metrics, logging, and alerting to enable observability and rapid diagnosis of production issues
Participate in on-call rotations, investigate production incidents, and contribute detailed retrospectives to prevent recurrence
Collaborate with cross-functional partners in product engineering, infrastructure, and data science to define requirements and deliver systems solutions end-to-end
Conduct and participate in code reviews to maintain code quality, share knowledge, and identify subtle correctness and performance issues including those introduced by AI-assisted development
Manage staged rollouts of systems changes using feature flagging and incremental deployment strategies, monitoring key reliability metrics throughout
Contribute to technical documentation for new systems components and updates to existing infrastructure, ensuring clarity for current and future owners
Identify and eliminate technical debt by consolidating duplicative implementations, deprecating obsolete code, and modernizing legacy systems components