# Hardware Architecture Expert - 3P

**Company:** [OpenAI](https://hotfix.jobs/companies/openai)
**Location:** San Francisco, CA, Seattle, WA
**Role:** Hardware Engineering
**Salary:** $342k – $555k/yr
**Skills:** GPU, Accelerator Architecture, Hbm, Nvidia, Amd, Benchmarking, Profiling, Fp16, Bf16, Fp8, Asics, Performance Analysis, Cache Hierarchies, Interconnect, Silicon Bring-Up
**Posted:** 2026-04-20

> 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.

## Job Description

## 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.

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