Performance and Capacity Engineer

Summary

Meta is seeking a Performance and Capacity Engineer to join the Capacity team to focus on AI performance and roadmap planning projects. This person would be required to work cross-functionally with a number of teams to ensure optimal operation and growth of our AI computing resources from both a cost and technology perspective. Tens of billions of user requests, hundreds of peta bytes of data, thousands of giga bps of network flow. Help build one of the largest AI training and inference services in the world!

Required Skills

Performance and Capacity Engineer Responsibilities:

• Scale the largest AI capacity in the world, identify scaling issues and solve them
• Work with Product Engineering, Infrastructure Engineering, and Data Engineering teams to find the optimal way to scale the AI infrastructure
• Tackle the state-of-the-art AI hardware/software performance issues: Analyze and debug difficult AI server performance issues (latest in industry), identify bottlenecks and optimize product/service performance to improve user experience
• Provide deepest visibility to what is going on for all products on AI infrastructure: Run capacity and performance experiments to determine scaling and utilization parameters for various service tiers
• Find the AI game changers and bring them on: Work with financial analysts, operations and engineering to perform cutting-edge technologies investigation and cost analysis for AI
• A lot of other cool work: Identify AI capacity-related issues proactively and work with systems, network, application operations and engineering teams to discover resolutions

Minimum Qualification

Minimum Qualifications:

• 12+ years of experience in performance or software engineering
• Bachelor's degree in Computer Science, Computer Engineering, relevant technical field, or equivalent practical experience.
• 12+ years of experience in coding & scripting languages such as C, C++, C #, Java, PHP, Python
• Experience working with large scale AI/ML systems (GPU based)

Preferred Qualification

Preferred Qualifications:

• Deep knowledge on GPU architecture
• Deep knowledge on AI training and inference
• MS or PhD degree in Computer Science, Electrical Engineering, Operations Research or other technical field

EOE

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