Product Manager (AI Hardware)

Artificial Analysis, Inc.•San Francisco, CA

About The Position

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, Amazon), Adam D'Angelo (Quora, Poe, OpenAI), Clem Delangue (Hugging Face) and other industry leaders. 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 every dollar of compute), this role offers a front-row seat to the next wave of AI.

Requirements

Strong analytical and critical thinking skills
Excitement about AI and eagerness to work at the forefront of technological innovation
Demonstrated excellence, both academically and professionally
Real interest in AI hardware and the silicon powering modern AI

Nice To Haves

Experience at an AI hardware startup, inference/infrastructure company, or chip company (e.g. Cerebras, Groq, SambaNova, Tenstorrent, Together AI, Fireworks, Baseten, or similar) — especially in roles touching AI accelerators or inference hardware
Experience with GPU/accelerator performance benchmarking, characterization, or systems performance engineering
Understanding of inference economics and price-performance — cost-per-token, utilization, total cost of ownership of compute
Experience in strategy consulting
Academic success in a technical degree programme, especially related to AI, computer architecture, or hardware (e.g. Computer Science, Electrical/Computer Engineering), or in a related technical field (e.g. Physics, Maths)
Strong writing skills, especially in relation to explaining technical topics to both technical and non-technical audiences
Visual communication skills (e.g. making slides, visualizing data)
A strong perspective on AI scaling laws — and specifically their implications for inference hardware and compute demand
Familiarity with the inference software stack (e.g. CUDA, TensorRT, vLLM, or similar serving frameworks) and how it interacts with underlying hardware
Familiarity with modern AI language models (e.g. developing systems leveraging LLMs as a component)
Experience working in software engineering or data science
Proficiency in Python and data analysis libraries (e.g. pandas, numpy, matplotlib); experience with AI/ML frameworks (e.g. PyTorch, TensorFlow)

Responsibilities

Contribute to AI hardware product strategy and help execute the roadmap for our accelerator evaluation platforms, covering GPUs, TPUs, custom inference silicon, and emerging architectures — including working with engineers on our team to develop features and potentially coding features yourself
Build and maintain comprehensive benchmarking methodologies that measure throughput, latency (e.g. tokens per second, time-to-first-token), and price-performance across accelerators and inference configurations, reflecting how the industry actually deploys models in production today and how it will in the coming months and years
Ensure that coverage of chips and accelerators across Artificial Analysis stays up to date by benchmarking new silicon and providers as they launch — both working with our development team and using our internal tooling yourself
Deeply understand the economics of AI inference — how cost-per-token, utilization, and hardware choice interact — and translate these insights into evaluation frameworks that help users make real deployment and procurement decisions
Help amplify the impact of our hardware benchmarks, collaborating with our team to maximize impact of our content across platforms and audiences
Work with leading chipmakers and inference providers to benchmark their accelerators, understanding their capabilities and helping shape industry standards for how AI hardware performance and efficiency are evaluated
Embrace an AI-native workflow, using the latest AI tools to generate leverage in a fast-changing industry and maintain our competitive edge in AI benchmarking