Compute Finance and Strategy

Own Your Intelligence

Prime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.

Our platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.

Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.

Your Role

Compute is the foundational input of the AI era. The companies, models, and capabilities that define the next decade will be shaped by who has access to compute, on what terms, at what economics, and how it gets allocated across the systems that get built on top of it. The financial and operational architecture for an asset class of this consequence is still being built, and the playbooks for navigating it don't yet exist. The people who write them will define how the AI infrastructure industry develops over the next decade.

You will own the analytical foundation for how we understand global compute markets: pricing supply across regions and term lengths, modeling the economics of large GPU commitments, evaluating neoclouds and hyperscalers, and turning that work into provider decisions, commercial structures, and customer-facing products.

The work sits at the intersection of infrastructure, finance, and AI systems. You will evaluate questions like when an H200 cluster is the right fit versus GB200 or GB300, how networking and storage constraints affect real workload performance, how utilization assumptions change the economics of a multi-year commitment, and how regional power, colo, and capital costs flow through to GPU-hour pricing. You will diligence providers not just on headline price, but on delivery timeline, cluster architecture, reliability, support model, contractual risk, and ability to serve frontier AI workloads.

The decisions you support will directly shape Prime Intellect’s ability to deliver high-quality compute to researchers, AI labs, and enterprises building on top of our stack.

Responsibilities

Compute Economics

• Build and own the financial models that price our compute supply: per-cluster economics, contract structures, hardware generation comparisons, geographic and provider differentials

• Model the economics of every meaningful supply decision — reserved vs. spot tradeoffs, term length, commitment level, hardware generation, provider mix, geography

• Own margin architecture: margin by workload, customer, product, and contract, so we always know what's actually profitable and where the leverage is

• Model the long-term P&L consequences of today's supply bets under multiple demand and pricing scenarios

Strategic Bets & Capital Allocation

• Partner with leadership on the biggest decisions the company makes: which providers to commit to, which hardware generations, what geographies to lean into, how aggressively to scale

• Build the financial frameworks that turn ambiguous strategic questions into decisions we can make with conviction

• Own the long-range plan, scenario models, and capital allocation framework across compute, headcount, and product investment

Provider Engagement & Diligence

• Engage directly with neoclouds, hyperscalers, and emerging providers on economic and technical diligence

• Run the financial side of supply qualification — what we accept, what we reject, what we negotiate harder on

• Translate technical performance characteristics into commercial recommendations

• Build the repeatable analytical process for evaluating new entrants to the global supply market

Market Intelligence

• Track pricing, availability, and provider dynamics continuously across every major market

• Build Prime Intellect's view of the global compute market — who's credible, who's mispriced, where supply is tightening, where the next wave of capacity is coming online

• Develop the analytical basis for our market positioning: when to commit hard, when to hold flexibility, where to lean in geographically

Cross-functional Partnership

• Partner with Strategic Finance on how compute economics flow through to the company P&L

• Partner with Engineering on the technical performance characteristics that drive cluster economics

• Partner with Sales and Product on pricing strategy for consumption-based and hybrid products

• Build board-ready analyses on supply strategy, capital allocation, and market positioning

What We're Looking For

• 4–7+ years in roles that combine financial rigor with real-world strategic or operational engagement. Backgrounds we'd find compelling include:

  • Investment banking, private equity, or growth equity with exposure to infrastructure, cloud, semiconductors, or technology

  • Quantitative or strategist roles at hedge funds, commodities desks, or trading firms

  • Infrastructure investing, project finance, or structured credit

  • Strategic finance or BizOps at a high-growth cloud, AI infrastructure, or compute-intensive company

• Exceptional modeling and analytical skills — you build the models yourself, and your models reflect how the business actually works

• Genuine technical curiosity. You don't need deep technical background to start, but you should be excited to develop fluency in GPU architectures, networking, cluster performance, and what makes one piece of compute economically different from another

• Strong commercial and strategic judgment — you understand that finance's job is to drive better decisions, not produce more analysis

• Comfortable engaging directly with vendors, partners, and senior counterparts at provider companies

• Ability to operate across registers — building rigorous models, briefing leadership on strategic implications, and running diligence with senior counterparts at provider companies

• 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

Bonus:

• Direct experience modeling datacenter, colocation, cloud, or power/energy economics

• Background covering AI infrastructure, cloud providers, semiconductors, or compute marketplaces from the banking, investing, or trading side

• Hands-on experience with cluster benchmarking, training/inference workload economics, or compute marketplaces

Why This Role

Compute economics is becoming one of the most consequential domains in technology, and almost no one is approaching it with the rigor it deserves. You'll be in the room for the decisions shaping Prime Intellect's future and, in real ways, the future of open AI infrastructure. You'll work directly with leadership on the calls that define the company, develop deep expertise in a market most finance professionals only read about, and build a foundation in compute economics that is increasingly valuable across the industry.

What We Offer

• Cash Compensation Range of $200-300k + meaningful equity

• Flexible work (remote or San Francisco)

• Visa sponsorship and relocation support

• Professional development budget

• Team off-sites and conferences

• A front-row seat to building the infrastructure layer for open AI