$200K – $300KSeniorFull Time
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, Tri Dao, Dylan Patel, Clem Delangue, Emad Mostaque, and many others.
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.
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
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.
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
