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# Production Systems Engineer, AI Systems

- **Company:** Facebook Inc.
- **Location:** Austin, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Systems Engineering, Big Data, Computer Clusters, Code Coverage, Data Centers, Data Center Infrastructure Management (CIM), Software Debugging, Linux, Dynamic Random-Access Memory, Firmware, Hardware Design, InfiniBand, PCI Express, Remote Infrastructure Management, Software Deployment, Subsystems, System Testing, Strategies of Testing, AI Infrastructure, Graphics Processing Unit (GPU), Application Specific Integrated Circuits, AI Platforms, Hardware Acceleration, Server Operating Systems & Platforms, Programming Languages
- **Published:** September 2, 2026
- **Apply:** https://www.jobmonkeyjobs.com/career/27985493/Production-Systems-Engineer-Ai-Systems-Texas-Austin-7418

## About the Role

* 6+ years of experience in hardware systems engineering, silicon validation, firmware validation, or system-level bring-up for AI servers, GPUs, TPUs, or AI accelerator platforms
 * Experience in one or more of the following domains: ASIC bring-up and characterization, board-level debug, firmware validation, or large-scale system validation in data center environments
 * Experience developing test specifications, validation procedures, and debug methodologies for complex hardware systems
 * Experience leading root-cause analysis and troubleshooting of system-level failures across hardware, firmware, and software stacks
 * Experience with high-speed interconnects or memory subsystems such as PCIe, NVLink, DDR5, or HBM in the context of AI or HPC system validation
 * Experience analyzing system telemetry and fleet health data to identify reliability trends and drive engineering improvements, * Proficiency in scripting or programming languages such as Python for automation of infrastructure workflows and data analysis
 * Familiarity with Linux-based server environments and data center management tooling used in large-scale production operations
 * Experience defining hardware-software interface requirements for telemetry, out-of-band management, or remote diagnostics in data center AI systems
 * Experience with high-speed interconnects and memory subsystems such as PCIe, NVLink, InfiniBand, DDR5, or HBM in the context of AI or HPC infrastructure operations

## Description

Meta is seeking a Hardware Systems Engineer to support the new product introduction (NPI) of next-generation AI and high-performance computing infrastructure for large-scale data center deployments. In this role, you will work at the intersection of server systems, AI applications and data center operations, partnering with hardware design, firmware, software, networking, and capacity engineering teams to validate and scale cutting-edge AI hardware systems from early bring-up through production readiness., * Lead end-to-end system validation strategies for AI and HPC hardware platforms, including AI accelerators, GPU clusters, and high-bandwidth memory subsystems in data center environments

 * Drive hands-on bring-up, characterization, and validation of AI server systems and associated components such as PCIe, NVLink, DRAM, and high-speed networking fabrics
 * Develop and maintain test specifications, validation procedures, and debug guides tailored to AI infrastructure NPI programs
 * Investigate and root-cause complex system failures spanning silicon, firmware, software, and hardware layers in collaboration with cross-functional engineering teams
 * Triage and track hardware and firmware defects through resolution while maintaining forward progress on NPI program milestones
 * Identify gaps in test coverage and drive improvements to test methodologies, tooling, and automation frameworks across the NPI lifecycle
 * Partner with AI platform and capacity engineering teams to define acceptance criteria and deployment readiness standards for new AI hardware systems
 * Guide data collection, analysis, and reporting efforts to surface systemic hardware quality trends and inform go/no-go decisions for production deployment
 * Communicate validation status, risk assessments, and technical findings to internal engineering teams and external hardware vendors
 * Collaborate with firmware and software teams to define hardware-software interface requirements for telemetry, diagnostics, and remote management of AI infrastructure


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