Meta is seeking an experienced Production Systems Engineer to join our Release to Production (RTP) team. Our servers and data centers are the foundation upon which our rapidly scaling infrastructure operates efficiently to deliver our innovative services. The RTP team is responsible for the Hardware Lifecycle of all Meta servers including pre-production hands-on system and hardware debugging and stress testing, enabling production-ready system monitoring, automated provisioning and automated remediation of issues. RTP Engineers work closely with hardware designers, system manufacturers, component vendors, capacity engineering, production engineering, Facebook services, and data center operations teams to test systems before release to our production data centers, and to track the health and life cycle of servers in production. Production Systems Engineers in this role build tooling and infrastructure to support the validation of upcoming and existing platforms in the AI space.

Production Systems Engineer, Tooling Responsibilities

• Enhance existing tooling and validation frameworks to support cluster scale and to enable cluster-level Hardware validation.


• Develop CI/CD pipelines to test clusters of AI servers.


• Integrate AI workloads into early validation phases for new Hardware platforms to left-shift testing.


• Support AI workload enablement at Meta ODM / Vendor partners.

Minimum Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.


• 4+ years of Software Development experience in a high-level language (e.g., Python, Rust, Go).


• 2+ years of experience in working with AI/HPC systems, including hardware and software components, networking, co-design.


• Familiarity with hardware design and NPI development lifecycle.


• Proven UNIX/Linux experience and skills to debug complex issues.

Preferred Qualifications

• 6+ years of Software Development experience in a high-level language (e.g., Python, Rust, Go).


• 4+ years of experience in working with AI/HPC systems, including hardware and software components, GPUs, communication libraries (e.g., NCCL / MPI).


• Experience with distributed workload schedulers such as Kubernetes or Slurm.

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