Software Engineer, Systems

Meta is seeking a Software Engineer specializing in systems software to drive technical direction across large-scale infrastructure and platform engineering. The team responsible for the foundational runtime that powers containers and virtual machines across Meta’s compute fleet.

This team builds the host-level systems that launch, manage, isolate, monitor, and terminate workloads. Its software coordinates workload artifacts and compute resources while enforcing performance, reliability, security, and resource-isolation boundaries. The runtime sits at a critical layer between applications and the underlying host infrastructure.

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.

• Set and drive the technical direction for container runtime and isolation stack.
• Improve fleet-wide efficiency and reliability of container-based workloads: resource enforcement, noisy-neighbor isolation, and density at millions-of-servers scale.
• Extend runtime and isolation primitives to support AI training and inference workloads (accelerator isolation, large-memory and high-bandwidth jobs, gang-scheduled work).
• Partner with kernel, scheduler, capacity, and hardware teams on hardware-software co-design and multi-generational fleet transitions.
• Mentor engineers, drive cross team design review, and represent teams container strategy within the Org Experience adhering to and implementing responsible, ethical AI practices (e.g., risk assessment, bias mitigation, quality and accuracy reviews).
• Experience leading large-scale platform migrations or systems modernization efforts in high-availability production environments.
• Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact (e.g., efficiency gains, quality improvements).
• Demonstrated ongoing AI skill development (e.g., prompt/context engineering, agent orchestration) and staying current with emerging AI technologies.
• Experience with operating system internals such as scheduler design, memory subsystems, or kernel-userspace interfaces.
• Experience with distributed systems consensus protocols such as Raft or Paxos, or building highly available and fault-tolerant infrastructure.