---
title: 'Compute Finance and Strategy at Prime Intellect'
canonical: 'https://feeny.ai/job/compute-finance-and-strategy-prime-intellect-san-francisco-d59anm4nfm4x'
type: 'job'
last_seen: '2026-09-07'
---

# Compute Finance and Strategy at Prime Intellect

- **Company:** Prime Intellect
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Posted:** 2026-07-08
- **Last confirmed live:** 2026-09-07
- **Apply:** https://jobs.ashbyhq.com/primeintellect/82084989-3721-475b-8253-46faab74a0ab

## Job description

## 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

## About Prime Intellect

## Company Overview
- **One-liner**: Prime Intellect provides an open, full-stack platform for companies to train, deploy, and continuously improve their own AI models through large-scale distributed reinforcement learning.
- **Entity Type**: Private (Series B)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2023
- **Founders**: Vincent Weisser (CEO), Johannes Hagemann (CTO)

## Core Business
- **Primary industry**: AI infrastructure, open-source AI research, and agentic training platforms
- **Target customers**: B2B — enterprises and AI labs that want to train custom models without building their own infrastructure; also open-source community contributors
- **Mission**: Democratize frontier AI training by making it accessible to every company and collectively owning the resulting open innovations.

## Products & Services
- **Compute**: Access to GPU clusters (H100, H200, B200+) from 50+ providers, plus on-demand single GPU instances and reserved clusters with InfiniBand networking and SLURM/K8s orchestration.
- **Hosted Training (Lab)**: Managed large-scale reinforcement learning (RL) training without infrastructure overhead, including a 2,500+ environment hub and custom eval benchmarks.
- **Inference**: Serverless or dedicated inference for custom models, with native LoRA support and 1-click deployment.
- **Open-Source Libraries**: `prime-rl` (async RL framework), `verifiers` (environments & evals), and environment hub for collaborative development.
- **INTELLECT-3**: A 100B+ parameter Mixture-of-Experts model trained on their RL stack, achieving state-of-the-art performance for its size.

## Market Standing
- **Valuation/Market Cap**: Not publicly disclosed
- **Total Funding**: $70.44M across multiple rounds (CBInsights, 2026); earlier reports indicated $20.5M in seed rounds, followed by a Series B round of $49.94M led by Founders Fund and other investors.
- **Notable Investors/Partners**: Founders Fund, CoinFund, Distributed Global, Radical Ventures, Andrej Karpathy, Clem Delangue (Hugging Face), Dylan Patel (SemiAnalysis), Tri Dao (Together AI).
- **Growth Signals**: Headcount grew 128.6% YoY to 36 employees; 24 active job postings (monthly job growth +26.3%); launched INTELLECT-3 and SYNTHETIC-2 datasets; customers include Ramp and Zap; operates in 9 countries.

## Competitive Advantages
- **Full-stack integrated platform** – compute, post-training, evals, and inference in one place, reducing the fragmentation of building and maintaining AI workflows.
- **Large-scale distributed RL expertise** – proven ability to train 100B+ models across clusters (global training via their RL stack).
- **Open-source ethos** – models, datasets, and libraries are released openly, attracting a community of researchers and developers.
- **Founding team & research edge** – strong ties to frontier AI research, with contributions like prime-rl and self-improving agent loops.

## Strategic Focus
- **Self-improving agents** – building infrastructure that closes the loop from deployment back to training, enabling models to compound performance over time.
- **Scaling RL at extreme sizes** – recent research (RL at 1T Scale) shows commitment to massive-scale reinforcement learning.
- **Enterprise adoption** – adding dedicated solutions engineers and managed workflows for corporate customers.

## Why Work Here
- **Culture**: Seeks “the most ambitious developers” and emphasizes cutting-edge research and engineering. The team includes former engineers from Together AI, Aleph Alpha, and Anyscale.
- **Remote/Hybrid**: Remote-friendly with HQ in San Francisco.
- **Growth trajectory**: Rapidly scaling company (headcount up 128% YoY) with multiple open roles across engineering, research, and go-to-market.
- **Tech stack**: Python, PyTorch, CUDA, Kubernetes, SLURM, Grafana, and a broad modern AI toolchain – opportunity to work on infrastructure-level challenges.

## Sources
1. [primeintellect.ai](https://www.primeintellect.ai/)
2. [linkedin.com/company/primeintellect-ai](https://www.linkedin.com/company/primeintellect-ai)
3. [jobs.ashbyhq.com/PrimeIntellect](https://jobs.ashbyhq.com/primeintellect)
4. [cbinsights.com/company/prime-intellect](https://www.cbinsights.com/company/prime-intellect)
5. [docs.primeintellect.ai/introduction](https://docs.primeintellect.ai/introduction)

## Other roles at Prime Intellect

- [Head of Talent](https://feeny.ai/job/head-of-talent-prime-intellect-san-francisco-naf4r2rwx21x) — San Francisco, CA
- [Member of Technical Staff - Training Platform](https://feeny.ai/job/member-of-technical-staff-training-platform-prime-intellect-san-francisco-4btkkz3tw3ps) — San Francisco, CA
- [Member of Technical Staff - Sandbox Platform](https://feeny.ai/job/member-of-technical-staff-sandbox-platform-prime-intellect-san-francisco-qsr7247a01ar) — San Francisco, CA
- [Member of Technical Staff - Inference](https://feeny.ai/job/member-of-technical-staff-inference-prime-intellect-7syapkjxntgq)
- [Member of Technical Staff - GPU Infrastructure](https://feeny.ai/job/member-of-technical-staff-gpu-infrastructure-prime-intellect-san-francisco-yxe0m7p9mjqy) — San Francisco, CA
- [Member of Technical Staff - Full Stack Software Engineer](https://feeny.ai/job/member-of-technical-staff-full-stack-software-engineer-prime-intellect-san-enhkkcn3cfge) — San Francisco, CA
- [Member of Technical Staff - Compute Platform](https://feeny.ai/job/member-of-technical-staff-compute-platform-prime-intellect-san-francisco-00b0zmttc2p0) — San Francisco, CA
- [Research Engineer - RL Infrastructure](https://feeny.ai/job/research-engineer-rl-infrastructure-prime-intellect-san-francisco-s38mrqzq4m59) — San Francisco, CA
- [Research Engineer - Reinforcement Learning](https://feeny.ai/job/research-engineer-reinforcement-learning-prime-intellect-san-francisco-q3tyazz9enkn) — San Francisco, CA
- [Research Engineer - Distributed Training](https://feeny.ai/job/research-engineer-distributed-training-prime-intellect-san-francisco-6anh0v1a8nqx) — San Francisco, CA