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OpenAIOpenAISan Francisco, CA

Hardware Architecture Expert - 3P

Evaluate GPU and accelerator architectures from vendors like NVIDIA and AMD, analyze tradeoffs in compute, memory, and interconnect for AI workloads, and validate performance through benchmarking and early silicon bring-up.

342k – 555k/yr
HybridHardware Engineering

About the role

Key Responsibilities

  • Engage deeply with silicon vendors (e.g., NVIDIA & AMD) on GPU and accelerator architecture tradeoffs.
  • Analyze and interpret performance, power, and efficiency characteristics of next-generation hardware.
  • Translate vendor specifications into expected real-world performance for AI workloads.
  • Evaluate architectural aspects including:
    • Compute throughput and utilization
    • Memory systems (HBM, cache hierarchies, bandwidth constraints)
    • Data types and precision tradeoffs (FP16, BF16, FP8, etc.)
    • Interconnect and scaling behavior
  • Run benchmarks and profiling to validate hardware performance against workload requirements.
  • Lead early bring-up and evaluation of engineering sample (ES) silicon.
  • Partner with performance modeling and system architecture teams to align measured vs. modeled behavior.
  • Provide actionable feedback to vendors to influence future silicon design and roadmap decisions.

Qualifications

  • Deep expertise in GPU or accelerator architecture, including performance and power tradeoffs.
  • Understand AI workload behavior and how it interacts with hardware design choices.
  • Comfortable engaging directly with silicon vendors at a technical architecture level.
  • Hands-on experience with benchmarking, profiling, and performance analysis.
  • Can translate low-level hardware details into system-level and workload-level impact.
  • Equally comfortable in theory (architecture) and practice (measurement/validation).
  • Thrive in environments where you bridge internal teams and external partners.

Preferred Skills

  • Experience working with or at companies like NVIDIA & AMD or similar silicon providers.
  • Familiarity with AI accelerator stacks, including GPUs, custom ASICs, or emerging architectures.
  • Experience with early silicon bring-up or hardware validation workflows.
  • Strong understanding of memory systems (HBM, DDR, cache hierarchies) and data movement bottlenecks.
  • Experience with performance tooling, microbenchmarks, and workload characterization.

Skills

GPUAccelerator ArchitectureHbmNvidiaAmdBenchmarkingProfilingFp16Bf16Fp8AsicsPerformance AnalysisCache HierarchiesInterconnectSilicon Bring-Up
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