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.
Responsibilities
- 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
Minimum Qualifications
- 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