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Product Manager (AI Hardware)

Artificial Analysis· Technical Staff (incl. Product)
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San Francisco (On-site)Full-timePosted 1mo ago

About the role

Product Manager role focused on benchmarking AI hardware accelerators, building and extending benchmarking frameworks, and translating hardware performance and economics into actionable analysis for industry stakeholders.

Responsibilities

  • Benchmark AI Accelerators: Design and execute performance benchmarking across GPUs, TPUs and custom inference silicon, measuring throughput, latency and price-performance the way the industry actually deploys
  • Build Evaluation Methodology: Develop and maintain the frameworks that define how AI hardware performance and efficiency are measured, extending products like AA-AgentPerf, from tokens per second and time-to-first-token to total cost of ownership
  • Analyze Inference Economics: Deeply understand how cost-per-token, utilization and hardware choice interact, and translate that into analysis the industry relies on for deployment and procurement decisions
  • Partner with Chipmakers: Work with the top hardware companies in the world, from NVIDIA, AMD and Google to the leading new accelerator companies, at a deep technical level: benchmarking their latest silicon, shaping methodology together, and setting the standards the industry measures by
  • Drive Strategic Analysis: Produce reports and data visualizations that communicate hardware performance and economics to technical and non-technical audiences
  • Become AI-Native: Embrace an AI-native workflow, using cutting-edge AI tools to generate leverage in a fast-changing industry and maintain our competitive edge in AI benchmarking

Qualifications

  • 3+ years of professional experience, including at least 2 years at an AI chip company (e.g. NVIDIA, Cerebras, SambaNova, Groq or similar)
  • Strong analytical and critical thinking skills
  • Proficiency in Python and data analysis
  • Deep familiarity with AI accelerators and the inference software stack (e.g. CUDA, TensorRT, vLLM or similar serving frameworks)
  • Understanding of inference economics: cost-per-token, utilization, and the trade-offs that drive real deployment decisions
  • Genuine, demonstrable interest and knowledge of Frontier AI. We want people who have informed opinions about where AI is heading, not just people who use AI tools

Skills

Required:Benchmarking design and executionPythonData analysisAI accelerators knowledge

Benefits

  • Competitive compensation including equity
Last updated Oct 1, 2026. Interested? Apply on the company site.

About Artificial Analysis

Independent analysis of AI

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