Artificial Analysis
Technology
ProductManager(AIHardware)
Neural analysis suggests this role is
optimal for Manager candidates.
“Product Manager (AI Hardware) at Artificial Analysis. Skills: AI hardware, Product management, Benchmarking. Contribute to AI hardware product strategy. Execute roadmap for accelerator evaluation platforms”
Industry & Context.
Analytical thinking; Critical thinking
What They're Looking For.
Must Have
Analytical and critical thinking skills, Excitement about AI, Eagerness to work at forefront, Demonstrated excellence academically, Demonstrated excellence professionally, Real interest in AI hardware
Nice to Have
Experience at AI hardware startup, Experience at inference/infrastructure company, Experience at chip company, Experience with GPU/accelerator benchmarking, Experience with systems performance engineering, Understanding of inference economics, Experience in strategy consulting, Academic success in technical degree, Writing skills, Visual communication skills, Perspective on AI scaling laws, Familiarity with inference software stack, Familiarity with modern AI language models, Experience working in software engineering, Experience working in data science
What You'll Do.
Contribute to AI hardware product strategy
Execute roadmap for accelerator evaluation platforms
Develop features with engineers
Code features yourself
Build and maintain benchmarking methodologies
Measure price-performance
Benchmark new silicon
Benchmark new providers
Understand AI inference economics
Translate insights into evaluation frameworks
Amplify impact of hardware benchmarks
Collaborate to maximize content impact
Work with inference providers
Benchmark accelerators
Understand accelerator capabilities
Shape industry standards
Embrace AI-native workflow
Use AI tools for leverage
How You'll Work.
Team & Collaboration
Cross-functional collaboration; Collaborating with engineering; Collaborating with analysis colleagues; Collaborating with founders; Collaborating with team; Collaborating with product
Communication Scope
Explaining technical topics
Process & Methodology
Roadmap planning
Full Job Description
ABOUT ARTIFICIAL ANALYSIS Artificial Analysis is the leading independent AI benchmarking company. We support labs, engineers and enterprises to understand AI capabilities and make critical decisions about their AI strategies. We are the go-to authority for understanding AI, from AI labs and enterprises to media, investors, and policymakers. Our benchmarks don't just measure the cutting edge of AI, they are actively shaping the frontier. Our benchmarks and analysis are trusted by hundreds of thousands of users and are the go-to reference for leading AI labs including OpenAI, Google, Meta, NVIDIA and Anthropic, and major publications including the Wall Street Journal, Bloomberg, the Financial Times and The Economist. We are a team of 35+, on track to triple by year end, backed by Nat Friedman (Github, Meta), Daniel Gross (SSI), Andrew Ng (Google Brain, DeepLearning.ai http://DeepLearning.ai, Amazon), Adam D'Angelo (Quora, Poe, OpenAI), Clem Delangue (Hugging Face) and other industry leaders. THE OPPORTUNITY We're hiring for a Product Manager focused on AI hardware — the inference chips and accelerators that the entire AI industry runs on (GPUs, TPUs, and the growing wave of custom silicon). You'll work closely with us to develop and enhance our hardware benchmarks and leaderboards, contributing to product strategy and execution at the intersection of silicon and AI. This is a hands-on, technical product role: you'll drive day-to-day operations for our hardware benchmarking offering and be a key contributor to product development, collaborating closely with engineering and analysis colleagues, as well as our founders. You'll design and run performance benchmarks, build cost and throughput models, and work with top chipmakers and inference providers to benchmark their latest accelerators. If you're excited about cutting-edge AI silicon and care deeply about how the economics and performance of inference are evolving (from hyperscale deployments to startups optimizing ev
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