Principal AI Cluster Performance Validation Engineer

Amd

Confirmed live 4 days ago High trust

Quick summary

Work type
On-site
Location
Austin, TXSeattle, WASanta Clara, CASecaucus, NJ
Salary
$200,000–$300,000 / yr
Posted
54 days ago
Freshness
Confirmed live 4 days ago
Closes
Sep 9, 2027

Market check

Salary context

Above market

How this pay compares to similar roles

Similar $216k
This role $250k
$147k $316k
below market most similar roles pay here above market

This role pays more than 75% of similar roles. Most pay $177,300–$254,750 — the blue band above. At the midpoint, this role pays about $250k versus about $216k for comparable roles.

Based on 240 similar postings.

Employer

About Amd

AMD (Advanced Micro Devices) is a semiconductor company that develops high-performance processors, graphics cards, and adaptive computing solutions for gaming, data centers, and embedded markets. Industry: Semiconductors

Amd currently has 506 open roles on FindRole.

Listed pay typically runs $172,000–$258,000 across 506 roles with salary data.

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View all roles at Amd

At a glance

TL;DR · Principal AI Cluster Performance Validation Engineer

The Principal AI Cluster Performance Validation Engineer joins a dynamic team focused on optimizing and achieving peak performance for GPU clusters. This role involves evaluating scalability under various workloads, developing benchmarking strategies, and identifying bottlenecks within GPU cluster environments. The engineer will focus on RDMA networks, specifically analyzing data flows between GPUs, NICs, and cluster networks. Key responsibilities include performance profiling, tuning RoCE v2 network congestion, and implementing parallel processing optimizations. The candidate must utilize Python, Bash, and Linux kernel networking expertise to automate performance analysis and debug complex hardware and firmware configurations. This position addresses the technical challenge of enhancing RDMA throughput and collective communications within large-scale parallel clustered computing systems to ensure optimal performance across diverse networking technologies and cluster sizes.

What you'll do

  • Evaluate GPU cluster scalability by conducting thorough testing under various workloads and networking technologies.
  • Develop and execute comprehensive benchmarking strategies to assess baseline performance and identify bottlenecks.
  • Optimize RDMA throughput, RoCE v2 network congestion, latency, and collective communications within GPU clusters.
  • Utilize profiling tools and methodologies to analyze performance bottlenecks and provide actionable insights.
  • Implement optimization strategies including protocol enhancements, load balancing, and parallel processing optimizations.
  • Create detailed documentation of performance analysis, tuning efforts, and outcomes for internal stakeholders.
  • Debug complex hardware, firmware, and clustered configurations to resolve performance issues.

What we're looking for

  • Bachelor's or Master's degree in computer science or electrical engineering.
  • Experience debugging complex HW/FW and clustered configurations.
  • Experience in system level performance tuning and debug methodologies.
  • Experience in optimizing the performance of GPU clusters (preferred).
  • Experience with RDMA network configuration, troubleshooting, and performance tuning (preferred).
  • Proficiency in scripting languages like Python or Bash for automation and performance analysis (preferred).
  • Linux kernel networking expertise (preferred).
  • Experience with machine learning and/or HPC system design (preferred).

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