Building Open Superintelligence Infrastructure
Prime Intellect is building the open superintelligence stack — from frontier agentic models to the infra that enables anyone to create, train, and deploy them. We aggregate and orchestrate global compute into a single control plane and pair it with the full RL post-training stack: environments, secure sandboxes, verifiable evals, and our async RL trainer. We enable researchers, startups and enterprises to run end-to-end reinforcement learning at frontier scale, adapting models to real tools, workflows, and deployment contexts.
We recently raised $15mm in funding (total of $20mm raised) led by Founders Fund, with participation from Menlo Ventures and prominent angels including Andrej Karpathy (Eureka AI, Tesla, OpenAI), Tri Dao (Together AI), Dylan Patel (SemiAnalysis), Clem Delangue (Huggingface), Emad Mostaque (Stability AI) and many others.
Your Role
You will own compute at Prime Intellect - the sourcing, economics, contracting, and strategic positioning of the GPU supply that powers everything we train, serve, and sell.
This is one of the most consequential roles at the company, and one of the most consequential compute roles in the industry. Compute is our product, and compute is the constraint that shapes what the open AI ecosystem can do. Every frontier lab is fighting for the same GPUs; your job is to make sure the open ecosystem has a leading seat at the table.
The decisions you make will shape the industry. Which neoclouds get the capital and commitments to scale. Which geographies become meaningful compute hubs. Which hardware generations get broad access versus staying locked inside closed labs. Which open models are economically possible to train and serve. You'll co-design the compute layer of the open model ecosystem alongside our research and engineering teams — deciding, together, what we train, on what, where, and at what cost structure.
You'll work directly with leadership team and founders of the neoclouds reshaping global compute, and with the research and engineering teams pushing the frontier of open post-training. You'll structure the commercial relationships that define the next several years of AI infrastructure, get first access to the latest generations of accelerators as they come online, and build the financial and operational architecture that turns a fragmented global supply market into Prime Intellect's durable advantage.
You need to be as comfortable modeling the unit economics of a three-year GB200 commitment against a volatile spot market as you are sitting with our research team to understand what an upcoming training run actually needs, or negotiating a nine-figure reserved capacity agreement with neocloud leadership.
Responsibilities
Compute Strategy & Economics
- Own the economics of compute end-to-end: the unit economics of every contract, the margin architecture across training and inference products, the long-term P&L consequences of today's supply bets
- Partner with Finance and leadership on capital strategy — how much to commit, to whom, for how long, on which hardware, with what balance sheet exposure
- Build the frameworks that turn supply decisions into clear financial outcomes, and that let us make multi-hundred-million-dollar bets with conviction under uncertainty
- Shape the commercial architecture of the open compute ecosystem: how committed capacity, spot markets, credit structures, and partner economics fit together
Sourcing & Contracting
- Own end-to-end procurement of GPU capacity globally — across hyperscalers, tier-one neoclouds, regional operators, and emerging providers in North America, Europe, the Middle East, and Asia
- Negotiate and close reserved capacity agreements, spot and burst arrangements, MSAs, DPAs, and order forms at nine- and ten-figure scale
- Secure early access to the latest generations of accelerators (B200, GB200, and what comes next) — in the quantities we need, before our competitors
- Build and maintain the senior relationships that make Prime Intellect the partner of choice for providers deciding where to allocate scarce capacity
Co-Design with Research & Engineering
- Work closely with our research team to translate training roadmaps, RL workloads, and open model ambitions into concrete compute requirements — and back the other way, to surface what's possible given the supply we can secure
- Partner with Engineering on acceptance testing, goodput validation, and the technical qualification of new providers and hardware
- Sit at the table where the biggest calls get made: which open models we train, which customers we serve, which bets are worth the capital
Market Intelligence & Positioning
- Track pricing, availability, and provider dynamics continuously across every major global market
- Serve as the internal source of truth on the compute market — who's credible, who's mispriced, where supply is about to tighten, which providers will still exist in 18 months, where the next wave of capacity is coming online
- Advise leadership on the strategic bets that define the company: which accelerators, which providers, which geographies, which contract structures, which moments to lean in hard
What We're Looking For
- Strong business and financial instincts — you think natively in unit economics, margin structure, and capital allocation, and you can model the long-term P&L consequences of complex supply decisions
- Deep fluency in the global AI compute market: you know the providers, the hardware generations and their real tradeoffs, the pricing dynamics, and where the market is going over the next 12–24 months
- Enough technical understanding to be dangerous — you can push back on a vendor's spec sheet, read a cluster topology diagram, understand why two nominally identical clusters deliver different goodput, and have a real conversation with researchers about what their workloads need
- Serious commercial chops: you've negotiated and closed contracts at meaningful scale, know how to find and use leverage, and understand how deal structure drives downstream economics
- Comfortable operating at the intersection of finance, commercial, product, and engineering — and translating fluently between all of them
- High ownership: you see gaps and build the fix before anyone asks
- AI-native in how you work: you use LLMs, automation, and programmatic tools to move faster