Meta
Production Systems Engineer, Fleet Strategy
$173K–$245K
Menlo Park, CA · Full-time · Posted Oct 2, 2026
- Experience
- 8+ years of hands-on software/firmware/hardware engineering to build systems/products for the IT industry.
- Education
- Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience; Master’s degree or PhD in Computer Engineering, Electrical Engineering, or related field is also listed.
About the job
Researches and improves hardware reliability, performance, and data-center efficiency across Meta’s server fleet, using data analysis and machine learning to diagnose failures and guide platform decisions.
What you'll do
- Analyze hardware and data-center data to identify, diagnose, and root-cause failures and efficiency issues.
- Develop experiments, analytical frameworks, monitoring, and predictive tools to support fleet optimization and partner-team decisions.
- Collaborate with engineering and operations teams on server architecture, AI platforms, silicon, thermal solutions, and future hardware designs.
What they ask for
- Bachelor's degree in Computer Science, Computer Engineering, a relevant technical field, or equivalent practical experience.
- 8+ years of hands-on software/firmware/hardware engineering to build systems/products for the IT industry.
- Troubleshooting and data-tooling experience, including data analysis, analytical models, and visualizations.
- Knowledge of server architecture and components; experience with Linux systems and server systems management.
Nice to have
- Master’s degree or PhD in Computer Engineering, Electrical Engineering, or related field.
- Experience with PCIe, Networking, Flash, Memory, CPU, GPU, or DRAM (DDR4/5 or HBM); SQL, Python, or C/C++ proficiency; lab-tool integration for automated workflows.
Pay
$173,000/year to $245,000/year USD base salary
Tools
SQL, Python, C/C++, Linux, PCIe, DRAM (DDR4/5 or HBM)
AI focus
Applies data-driven and machine-learning techniques to hardware characterization, failure analysis, and fleet efficiency; supports evolving AI platforms and large language model growth through performance, scalability, and efficiency optimization.
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