Full-Time
Compute Finance and Strategy
Prime Intellect
51-200 employees
Decentralized GPU compute marketplace for AI
Compensation Overview
$200k - $300k/yr
H1B Sponsorship Available
San Francisco, CA, USA
Remote
Remote or based in San Francisco; relocation support is available.
 Finance & Banking (2) |
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- 4–7+ years in roles that combine financial rigor with real-world strategic or operational engagement, including investment banking, private equity, growth equity, quantitative or strategist roles at hedge funds, commodities desks or trading firms, infrastructure investing, project finance, structured credit, or strategic finance or business operations at a high-growth cloud, AI infrastructure, or compute-intensive company.
- Exceptional modeling and analytical skills, including the ability to build models that reflect how the business actually works.
- Genuine technical curiosity and willingness to develop fluency in GPU architectures, networking, cluster performance, and the economic differences among compute resources.
- Strong commercial and strategic judgment focused on improving business decisions.
- Comfort engaging directly with vendors, partners, and senior counterparts at provider companies.
- Ability to build rigorous models, brief leadership on strategic implications, and conduct diligence with senior provider-company counterparts.
- Ability to identify gaps and build solutions proactively.
- Ability to use large language models, automation, and programmatic tools to work efficiently.
- Build and own financial models that price compute supply, including per-cluster economics, contract structures, hardware-generation comparisons, and geographic and provider differentials.
- Model supply decisions involving reserved versus spot capacity, term length, commitment level, hardware generation, provider mix, and geography.
- Own margin architecture by workload, customer, product, and contract.
- Model long-term profit-and-loss consequences of supply decisions under multiple demand and pricing scenarios.
- Partner with leadership on provider commitments, hardware generations, geographic focus, and scaling decisions.
- Build financial frameworks that convert ambiguous strategic questions into actionable decisions.
- Own the long-range plan, scenario models, and capital allocation framework across compute, headcount, and product investment.
- Engage directly with neoclouds, hyperscalers, and emerging providers on economic and technical diligence.
- Run the financial side of supply qualification, including acceptance, rejection, and negotiation decisions.
- Translate technical performance characteristics into commercial recommendations.
- Build a repeatable analytical process for evaluating new entrants to the global supply market.
- Continuously track pricing, availability, and provider dynamics across major markets.
- Build the company's view of the global compute market, including provider credibility, pricing, supply constraints, and upcoming capacity.
- Develop the analytical basis for market positioning, including commitment, flexibility, and geographic strategy.
- Partner with Strategic Finance on how compute economics flow through to the company profit and loss.
- Partner with Engineering on 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.
- 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.
Prime Intellect builds a decentralized, peer-to-peer platform for AI development. It operates Prime Intellect Compute, a GPU marketplace that aggregates resources from multiple cloud providers so users can access affordable compute time for AI projects. The Prime Intellect Protocol governs open-source AI with community ownership and governance, enabling anyone to contribute compute, capital, and code for distributed model training. Its goal is to democratize AI development by providing a scalable, marketplace-driven, globally distributed environment for training and deploying advanced models.
Company Size
51-200
Company Stage
Series A
Total Funding
$150.5M
Headquarters
Dover, Delaware
Founded
2024
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Simplify's Take
What believers are saying
- On July 8, 2026, Prime Intellect raised $130 million at a $1 billion valuation.
- TechCrunch said Ramp, Zapier, and Flapping Airplanes drove $100 million annualized revenue.
- Prime Intellect’s August 5 Prime Agent launch and August 17 blog show accelerating shipped products.
What critics are saying
- Prime Intellect’s status page logged major Hosted Training outages on July 11 and July 30, 2026.
- Its agent tooling executes user code without sandboxing, exposing customers to credential theft.
- AWS, Nvidia, and frontier labs can bundle cheaper RL and GPUs, collapsing margins by 2027.
What makes Prime Intellect unique
- Prime Intellect turns fragmented GPUs into a global marketplace for training and inference.
- Its open-source stack spans compute, environments, RL, evaluations, sandboxes, and deployment.
- BrowserEnv and Prime Agent extend that stack into browser tasks and long-horizon agents.
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Benefits
Company Equity
Flexible Work Hours
Remote Work Options
Relocation Assistance
Professional Development Budget
Conference Attendance Budget
Growth & Insights and Company News
6 month growth
↓ -10%1 year growth
↓ -8%2 year growth
↑ 13%Prime Intellect releases Prime Agent self-improving RLM framework. Prime Intellect has launched Prime Agent, an open-source self-improving agent framework based on Recursive Language Models and Continual Harness architectures. AIDeveloper44 Team Prime Agent introduces a recursive architectural approach to autonomous agent orchestration and self-improvement. * Prime Agent is an open-source coding and research harness designed for long-horizon autonomous tasks. * The framework utilizes a Recursive Language Model (RLM) abstraction that treats sub-agents as programmatic function calls within a persistent IPython REPL. * Equipped with Opus 5, the system achieved 95.5% on the ARC-AGI-3 benchmark, exceeding the reported human expert baseline. Introduction to Prime Agent. On August 5, 2026, Prime Intellect announced the release of Prime Agent, an open-source coding harness designed to leverage the reasoning capabilities of modern frontier language models. The system is built around two primary technical abstractions: the Recursive Language Model (RLM) and the Continual Harness. Unlike traditional agent frameworks that rely on fixed tool-calling schemas and static prompts, Prime Agent allows for the programmatic creation, modification, and orchestration of sub-agents and memory states. The project is available under the MIT License and is designed for general coding workflows, autonomous evaluation, and research collaboration. According to the development team, Prime Agent was created to address the limitations of earlier harness designs that often force models to work around rigid scaffolding rather than leveraging it for complex reasoning. The Recursive Language Model (RLM) abstraction. The core of Prime Agent is the Recursive Language Model, which conceptualizes context as a variable and sub-agent delegation as standard function calls. This is implemented through a persistent IPython REPL (Read-Eval-Print Loop) that provides the model with programmatic access to its own history, tools, and child agents. By writing language model programs as actions over its own context, the agent can navigate long-duration sessions without losing access to historical data stored in variables. Programmatic tool calling (PTC). In this architecture, the IPython kernel serves as the primary tool. Standard agent functions, such as file manipulation or shell commands, are executed as Python functions within this kernel. This allows models to parallelize tasks and manage sub-agents asynchronously. For example, a root agent can spawn a sub-agent using an await rlm call, which returns a child handle while the sub-agent performs its task in the background. Communication between these agents occurs through a dedicated messaging protocol (agent_message.send), allowing for complex orchestration without user intervention. Continual Harness and durable state. The second major abstraction, the Continual Harness, treats the agent's internal state - including prompts, skills, and memory - as a set of resources that can be refined through CRUD (Create, Read, Update, Delete) operations. This allows the agent to update its operating patterns based on evidence gathered during its trajectory. Through the /refine command, the system can apply updates to supplemental harness state while keeping the base system prompt immutable. This ensures that reusable patterns and lessons learned during a session can persist and be applied to future tasks. Session and memory management. To handle long-running tasks, Prime Agent implements a background daemon that manages all active sessions over a local socket. This allows users to attach or detach from a terminal session without interrupting the underlying agent loop. The system records session history as append-only JSONL files, enabling recovery from crashes by replaying the session state and kernel snapshots. For memory efficiency, sub-agents that remain inactive for more than 30 minutes are removed from memory and reloaded from disk only when addressed by another agent or the user. Benchmarking and performance. Prime Intellect reported that Prime Agent, when utilizing the Opus 5 model, achieved a score of 95.5% on the ARC-AGI-3 benchmark. This figure is notable for surpassing the established human expert baseline for the benchmark. The developers attribute this performance to the synergy between the harness design and the capabilities of current-generation frontier models, specifically their ability to operate within programmatic environments like IPython. Deployment and security considerations. Prime Agent is compatible with macOS and Linux and can be installed via a shell script provided by Prime Intellect. Once installed, the prime-agent command allows users to initiate sessions in local directories, browse running agents, and resume saved sessions. The framework supports various modes, including a text-user interface (TUI) for interactive use and headless modes (JSON/RPC) for automation. However, the documentation includes a security warning regarding the execution of model-generated code. Because Prime Agent executes Python and shell commands with the user's permissions, it does not function as a security sandbox. Users are advised to run the agent in restricted environments or external sandboxes when dealing with untrusted code or repositories. References & Sources
Prime Intellect has filed a notice with the US Securities and Exchange Commission to raise up to $85 million in new funding. The filing was made under Rule 504 or 506 of Regulation D or Section 4(a)(5) of the Securities Act of 1933. Prime Intellect operates as a full-stack platform and open research lab that makes frontier AI training accessible to companies. The company enables organisations to train their own models using its agentic training infrastructure. The company is led by chief executive officer Vincent Weisser. Federal securities law requires such notices to be filed within 15 days after the first sale of securities in the offering.
Prime Intellect: train frontier AI without Big Tech (full breakdown). #Quasa #QUA #PrimeIntellect Prime Intellect is building the Open Superintelligence Stack - a full-stack platform that lets anyone train, deploy, and continuously improve their own AI models without relying on Big Tech infrastructure. The company's mission is to make frontier-level AI accessible and collectively owned through decentralized compute, open-source tools, and community-driven development. At its core, Prime Intellect offers an integrated environment combining compute, reinforcement learning (RL) training, evaluations, inference, and secure sandboxes. Users can access thousands of community-built RL environments via the Environment Hub, run large-scale hosted training, deploy models with native LoRA support, and tap into a global marketplace of GPUs ranging from single nodes to massive clusters. The company has already delivered impressive technical achievements. It released INTELLECT-2, the first 32B-parameter model trained through globally distributed reinforcement learning, followed by INTELLECT-3 (a 100B+ Mixture-of-Experts model) and SYNTHETIC-2 (millions of collaboratively generated reasoning traces). Their open-source libraries - Verifiers and Prime-RL - enable asynchronous, fault-tolerant distributed RL at scale. Backed by leading investors and AI visionaries including Founders Fund, Radical Ventures, NVIDIA, Intel, Andrej Karpathy, John Schulman, and others, Prime Intellect combines cutting-edge research with practical infrastructure. Customers like Ramp and Zapier are already using the platform to build specialized agents that outperform frontier models in specific tasks. Prime Intellect is ideal for AI researchers, developers, startups, and enterprises that want to train and own their own models affordably while contributing to and benefiting from the open ecosystem. Highlights. * Truly decentralized and distributed training at scale. * Massive community of 2,500+ open RL environments. * End-to-end stack from compute to inference with seamless iteration loops. * Strong open-source contributions and real-world production use cases. Potential considerations. * As a rapidly evolving platform, some advanced features are still maturing. * Best results often require technical familiarity with RL and distributed systems. Overall Verdict: 4.6/5 stars Prime Intellect represents one of the most ambitious and technically sophisticated efforts to democratize superintelligence. By combining decentralized compute with powerful open tools, it is making it possible for more organizations and individuals to train and own frontier-level AI models. A highly promising platform for anyone serious about open and accessible AI development in 2026.
Modular agent development platforms. Prime Intellect Launches Its Full-Stack Agent Platform Published Jul 19, 2026 Prime Intellect developed a full-stack platform that helps companies build and train their own AI agents without relying entirely on frontier AI labs. The system combines hosted computing access, a reinforcement learning framework and evaluation tools, giving organizations the infrastructure needed to create task-specific agentic systems. The startup raised a $130 million Series A led by Radical Ventures, with participation from Nvidia Ventures, Intel Capital, Dell Technologies Capital and Iconiq. Its modular, marketplace-style platform lets customers select individual compute, training and evaluation components rather than committing to a single bundled system. Early customers include Ramp and Zapier, with Ramp reporting that an agent built using the platform outperformed frontier models on accuracy while operating faster and at a lower cost. For enterprises, Prime Intellect simplifies the technical requirements of developing proprietary AI systems while reducing dependence on closed-source providers. The platform also supports greater control over sensitive data and customized enterprise intelligence. Why this trend is growing. * Modular AI Agent Stacks - Composable compute, training and evaluation layers create room for enterprises to assemble proprietary agents that match specific workflows instead of relying on one-size-fits-all frontier models. * Enterprise Model Independence - Reduced dependence on closed AI labs is reshaping corporate AI strategy as businesses seek greater control over cost, performance, customization and sensitive data. * Reinforcement Learning Toolkits - Integrated RL frameworks and evaluation systems make task-specific agent optimization more accessible for companies building agents that outperform general-purpose models in narrow domains. Industries being reshaped. * Enterprise Software - Business platforms can embed custom-trained agents that automate specialized operations while improving accuracy, speed and cost efficiency for corporate users. * Cloud Computing - Hosted infrastructure providers gain new relevance as AI development shifts toward flexible marketplaces for compute, training resources and model evaluation services. * Financial Technology - Fintech companies can use proprietary agent systems to improve workflow automation, compliance handling and data-sensitive decision support without exposing core intelligence to external model providers. 7.1 out of 10 50% Men 50% Women Top markets: North America, Europe, Asia GENERATION * Gen Z * Gen Alpha * Millennial (primary audience) * Gen X (primary audience) POPULARITY Popularity 57% Activity 58% Freshness 99%
Prime intellect unveils open-source AI training stack. Will Brown of Primed and Loaded details the 'open superintelligence stack' for AI research, covering Verifiers, Prime RL, and the future of model post-training. Visual TL;DR. Primed & Loaded aims to Simplify AI Research. Simplify AI Research via Open Superintelligence Stack. Open Superintelligence Stack includes Prime RL. Open Superintelligence Stack includes Verifiers Library. Verifiers Library powers Environments Hub. Open Superintelligence Stack provides Global GPU Marketplace. Prime RL enables Enhance Open-Source Models. Verifiers Library enables Enhance Open-Source Models. * Primed & Loaded: Will Brown details open superintelligence stack at AI Engineer World's Fair * Simplify AI Research: mission to simplify large-scale open-source AI research for companies * Open Superintelligence Stack: comprehensive infrastructure stack for training and deploying AI models * Prime RL: open-source, full-stack training framework for asynchronous reinforcement learning * Verifiers Library: used to build environments and evaluate model performance with tasksets * Environments Hub: platform for creating and managing environments built with the Verifiers library * Enhance Open-Source Models: toolkit allows users to enhance open-source models for specific use cases * Global GPU Marketplace: over 10,000 GPUs available for compute resources in data centers Visual TL;DR Will Brown, Head of Applied Research at Primed and Loaded, recently presented the company's comprehensive AI research infrastructure stack, dubbed the 'open superintelligence stack.' This detailed dive, delivered at the AI Engineer World's Fair, focused on the post-training tools and libraries Primed and Loaded has developed, including Verifiers and Prime RL. The Open Superintelligence Stack. Brown outlined Primed and Loaded's mission to simplify large-scale open-source AI research and empower companies to train and deploy their own models. The company aims to provide a toolkit that allows users to enhance open-source models for their specific use cases. The 'open superintelligence stack' encompasses several key components: