Job description

Meta is seeking a Software Engineer specializing in systems software to drive technical direction across large-scale infrastructure and platform engineering. In this role, you will architect and evolve foundational systems — spanning operating system interfaces, runtime environments, distributed infrastructure, and low-level platform services — that underpin Meta's family of products at global scale. You will solve the most complex systems-level challenges across the organization, define multi-year technical strategy for platform reliability and performance, and serve as a force multiplier for engineering teams through deep systems expertise, AI-native workflows, and cross-functional technical leadership. Define and drive the long-term technical vision and architecture for large-scale systems software, including platform services, runtime environments, and low-level infrastructure components Architect systems-level solutions that address performance, reliability, and scalability requirements across distributed infrastructure serving billions of users Identify and resolve the most complex systems-level challenges spanning kernel interfaces, memory management, concurrency, inter-process communication, and distributed coordination Lead the design and implementation of critical platform components, establishing extensible technical foundations and coding standards that improve consistency and velocity across systems engineering teams Develop and operationalize testing frameworks, fault injection strategies, and verification methodologies that prevent systems regressions and improve production reliability at scale Define and track systems-level metrics, service level objectives, and performance guardrails that connect engineering outcomes to organization-wide priorities Leverage AI tooling and automation to accelerate systems development workflows, identify performance bottlenecks, and improve observability and debugging capabilities Partner with infrastructure, product, and platform engineering teams to translate complex systems requirements into durable technical designs, influencing roadmaps across organizational boundaries Drive safe and automated rollout strategies for major systems changes, including phased migrations and dependency coordination across teams Mentor engineers across the organization on systems design principles, performance profiling, debugging methodologies, and low-level platform internals Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience 8+ years of experience in systems software engineering, including design and implementation of operating system interfaces, runtime environments, distributed infrastructure, or low-level platform services Experience architecting and owning large-scale systems infrastructure used across multiple teams or organizations, including driving multi-year technical roadmaps Experience identifying and resolving complex systems-level performance, reliability, or correctness issues spanning concurrency, memory management, or distributed coordination Experience defining engineering standards, architectural patterns, and verification methodologies that improve systems quality and consistency at scale Experience communicating complex systems architecture and technical strategy in writing and presentations to both technical and non-technical stakeholders Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies Experience leading database migrations, schema evolution, or platform modernization efforts in large-scale production environments 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) Experience with database internals, like storage engines Experience with distributed database systems, consensus protocols (such as Raft or Paxos), or building highly available data infrastructure Experience applying AI and machine learning techniques to database optimization problems, such as query optimization, workload prediction, or automated performance tuning

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Keywords to mirror

aimachine learning

Platform: meta
Effort: medium

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