← All roles

Principal AI Cluster Performance Validation Engineer

AMD· Engineering
Apply on company site
Hybrid (Austin, TX)$200kFull-timePosted 1mo ago

About the role

Role focused on optimizing and validating performance of GPU clusters, with emphasis on RDMA networks (RoCE) between GPU, NIC and cluster network. Requires strong background in GPU architectures, parallel clustered computing, and hands-on system-level performance tuning and debugging.

Responsibilities

  • Scalability Testing: Evaluate the scalability of GPU clusters by conducting thorough testing under various workloads, ensuring optimal performance across different cluster sizes, configurations, and networking technologies (RoCE)
  • Benchmarking and Analysis: Develop and execute comprehensive benchmarking strategies to assess baseline performance, analyze bottlenecks, and identify areas for improvement within GPU cluster environments.
  • Cluster Network & Performance Optimization: Collaborate with hardware and software teams to enhance the overall performance of GPU clusters, focusing on aspects such as RDMA throughput, RoCE v2 network congestion, latency, and collective communications.
  • Performance Profiling: Utilize profiling tools and methodologies to analyze and identify performance bottlenecks, providing actionable insights for improvement.
  • Performance Tuning: Implement optimization strategies, including but not limited to protocol enhancements, load balancing techniques, and parallel processing optimizations.
  • Documentation: Create detailed documentation of performance analysis, tuning efforts, and outcomes, providing clear and concise reports for internal teams and stakeholders.
  • Collaboration: Work closely with cross-functional teams, including hardware engineers, software developers, and system architects, to integrate performance improvements into the GPU cluster architecture.
  • Continuous Learning: Stay current with the latest developments in GPU architectures, parallel processing, and emerging technologies to drive continuous improvement in GPU cluster performance.

Qualifications

  • Bachelor’s or Master’s degree in computer science or electrical engineering
  • Proven experience in optimizing the performance of GPU clusters
  • RDMA network configuration, troubleshooting and performance tuning
  • Strong understanding of GPU architectures, parallel computing concepts, and network protocols
  • Proficiency in scripting languages (e.g., Python, Bash) for automation and performance analysis
  • Experience with system level performance analysis tools and methodologies for GPU clusters
  • Analytical mindset with excellent problem-solving and debug skills
  • Familiarity with cluster management tools and systems
  • Excellent communication and collaboration skills for effective teamwork
  • Linux kernel networking expertise
  • Machine learning and/or HPC system design

Skills

Required:GPU cluster performance optimizationRDMA network configurationGPU architectures and parallel computingScripting (Python, Bash)

Benefits

  • Benefits offered are described: AMD benefits at a glance
Last updated Oct 8, 2026. Interested? Apply on the company site.
AMD logo

About AMD

together we advance_AI

AMD delivers leadership high-performance and adaptive computing solutions to advance data center AI, AI PCs, intelligent edge devices, gaming, & beyond.

AIData CenterHigh-Performance ComputingGamingEdge Devices
Company profileWebsite

More open roles at AMD