Hardware Architecture Expert - 3P
About the Team OpenAI’s Hardware organization develops system and infrastructure solutions optimized for advanced AI workloads. We collaborate across research, software, and external hardware partners to design and deploy next-generation AI systems at scale.
What this role actually needs.
About the Team OpenAI’s Hardware organization develops system and infrastructure solutions optimized for advanced AI workloads. We collaborate across research, software, and external hardware partners to design and deploy next-generation AI systems at scale. 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.) 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. - Have deep expertise in GPU or accelerator architecture, including performance and power tradeoffs. - Understand AI workload behavior and how it interacts with hardware design choices. - Are comfortable engaging directly with silicon vendors at a technical architecture level. Company context: OpenAI builds frontier AI systems, research infrastructure, and applied products for developers, enterprises, and global users.
Day-to-day expectations
OpenAI lists these responsibilities for the Hardware Architecture Expert - 3P role.
- 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.)
What a strong candidate brings
These requirements are extracted from the source listing and normalized for UpJobz readers.
- 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.
- Have deep expertise in GPU or accelerator architecture, including performance and power tradeoffs.
- Understand AI workload behavior and how it interacts with hardware design choices.
- Are comfortable engaging directly with silicon vendors at a technical architecture level.
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Source: jobs.ashbyhq.com · Source ID: 7af121a1-d29a-4745-84c1-ef1b58a3b840 · Confidence: 97/100 · Last checked: May 7, 2026
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