Prime Intellect

Role Description 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 - 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 - 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 - 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. Qualifications - 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 - Genuine technical curiosity - Strong commercial and strategic judgment - Comfortable engaging directly with vendors, partners, and senior counterparts at provider companies - Ability to operate across registers - High ownership - AI-native in how you work Requirements - 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 - Own margin architecture - Model the long-term P&L consequences of today's supply bets under multiple demand and pricing scenarios - Partner with leadership on the biggest decisions the company makes - Build the financial frameworks that turn ambiguous strategic questions into decisions - Own the long-range plan, scenario models, and capital allocation framework - Engage directly with neoclouds, hyperscalers, and emerging providers on economic and technical diligence - Run the financial side of supply qualification - Translate technical performance characteristics into commercial recommendations - Build the repeatable analytical process for evaluating new entrants to the global supply market - Track pricing, availability, and provider dynamics continuously across every major market - Build Prime Intellect's view of the global compute market - Develop the analytical basis for our market positioning - 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 Benefits - 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

Role Description You will own strategic finance at Prime Intellect - the models, analyses, and financial judgment that inform every major decision the company makes, from nine- and ten-figure compute commitments to GTM strategy to the next fundraise. This is not a traditional FP&A role. Compute is our largest cost, our core product, and the place where financial judgment has the highest leverage. You'll work on the operating economics of our supply: - Per-contract P&L across hyperscalers, neoclouds, and datacenter operators globally - Reserved vs. spot tradeoffs - Margin by workload, customer, and product You'll partner with the leadership team on capital allocation and every meaningful supply decision, with GTM on pricing and revenue planning, and with Engineering on the unit economics of everything we ship. You'll be the person who can model what a $200M reserved capacity commitment does to gross margin across three demand scenarios on a Tuesday, reconcile a neocloud partner's invoice against forecast on Wednesday, and partner with Sales on enterprise pricing on Friday. Responsibilities - Compute & Infrastructure Finance - Own the operating P&L for Prime Intellect's compute supply — per-contract economics across reserved capacity, spot, and hybrid arrangements - Partner closely with the leadership team on every meaningful supply decision: hardware generation, contract term, commitment level, provider mix, geography - Build and maintain per-contract cost forecasts; run budget-vs-actual; reconcile operating partner invoices to forecast - 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 - Serve as a finance counterpart in our operating relationships with neoclouds, datacenter operators, and other supply partners - Strategic Finance & Capital Allocation - Partner with leadership on the biggest financial decisions the company makes: compute commitments, hiring plans, product bets, fundraising - Own the long-range plan, scenario models, and the framework for how we allocate capital across compute, headcount, and product investment - Build the models that turn ambiguous strategic questions into decisions we can make with conviction - GTM Finance - Partner with Growth on revenue goals, sales capacity planning, deal desk, pricing, and marketing ROI - Build forecasting models that incorporate usage patterns, committed capacity, credit balances, and expansion signals - Inform pricing strategy for consumption-based and hybrid products - Fundraising & Financial Reporting - Build the financial narrative, models, and diligence materials for fundraising rounds - Build internal reporting: KPIs, financial performance, key bets and their status - Partner with the CEO on investor communications and strategic updates Qualifications - 5+ years across strategic finance, infrastructure investing, growth/private equity, investment banking, consulting, or operating finance roles at high-growth tech or AI infrastructure companies - Exceptional modeling and analytical skills — you build the models yourself, and your models reflect how the business actually works, not just accounting conventions - Strong commercial and business judgment — you understand that finance's job is to inform decisions, not just report on them - Comfortable with complex, usage-based, or infrastructure-heavy business models - Ability to operate at multiple altitudes — modeling a contract one hour, framing a strategic decision for the CEO the next, building a board deck after that, sitting across from a neocloud CFO the day after - Comfortable being the finance person in the partner/vendor room, partnering with technical operating teams - High ownership — you see gaps and build the fix before anyone asks - Comfortable in ambiguity and speed; this company and this market don't slow down - AI-native in how you work: you use LLMs, automation, and programmatic tools to build systems that scale without manual upkeep Bonus - Direct experience with AI infrastructure, datacenter, colocation, cloud, or power/energy economics and contract structures - Experience working on infrastructure operating P&L and cost-out programs - Background in AI infrastructure, cloud providers, or compute marketplaces - Fundraising experience at growth stage - Proficiency with financial modeling tools and languages (SQL, Python, Excel) Benefits - Competitive Cash Compensation + meaningful equity - Flexible work (remote or San Francisco) - Visa sponsorship and relocation support - Professional development budget - Team off-sites and conferences

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 What We Offer - Competitive Cash Compensation + 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

• Own threat modeling across our entire surface area: multi-tenant training infrastructure, sandboxed execution environments, API surfaces, and internal tooling • Design and implement zero-trust networking, identity, and access control across distributed GPU clusters and cloud infrastructure • Build secure-by-default patterns for our platform engineers — auth, secrets management, supply chain integrity, container hardening • Architect tenant isolation and data boundary enforcement for hosted RL training workloads (customers run arbitrary code in our environments) • Develop security frameworks specific to AI infrastructure: model weight protection, training data isolation, checkpoint integrity, gradient privacy • Secure the RL training loop end-to-end — from environment execution in sandboxes to reward signal verification and model artifact storage • Build detection and prevention for AI-specific attack vectors: prompt injection across agentic pipelines, model exfiltration, adversarial environment manipulation • Scope, manage, and run point on external penetration tests across our platform, hosted training infrastructure, and liquid compute layer • Build and maintain an internal red-teaming practice — automated and manual — targeting our most critical systems • Drive vulnerability management: triage, remediation SLAs, and root cause analysis • Build security monitoring and alerting across infrastructure (distributed clusters, Kubernetes, cloud) and application layers • Implement runtime security for containerized training workloads and sandboxed environments • Own incident response — build the playbooks, run the drills, lead the post-mortems • Design audit logging and forensic capability across all customer-facing systems • Drive SOC 2 Type II readiness and other compliance frameworks required by enterprise customers • Own the security narrative for customer-facing materials — questionnaires, architecture reviews, trust documentation • Partner with GTM to unblock enterprise deals that depend on security posture.

Head of Marketing & Developer Relations 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 (Chief Scientific Officer of Together AI), Dylan Patel (SemiAnalysis), Clem Delangue (Huggingface), Emad Mostaque (Stability AI) and many others. Your Role As Head of Marketing & Developer Relations, you'll own how Prime Intellect shows up in the world — the story we tell, the community we build, and how developers and researchers discover, adopt, and champion our platform. You'll shape our brand, run high-signal campaigns and launches, grow our developer community, and build the content engine that makes Prime Intellect the default starting point for RL infrastructure. This is a high-leverage role combining narrative craft, technical credibility, and community instinct — ideal for someone who can move fluidly between writing a launch post, scoping a conference strategy, and shipping a developer tutorial. Responsibilities Brand & Narrative - Define and evolve Prime Intellect's brand positioning, voice, and visual identity across all channels. - Translate deeply technical capabilities (async RL, distributed training, agentic post-training) into clear, compelling market narratives. - Own messaging frameworks for product launches, fundraise announcements, research releases, and partnership news. Developer Relations & Community - Build and lead the DevRel function — creating programs, content, and experiences that drive developer adoption and loyalty. - Grow and engage our open-source community (Environments Hub, prime-rl, Verifiers) through tutorials, office hours, hackathons, and contributor programs. - Develop and maintain developer documentation, quickstarts, and onboarding flows in close partnership with Product and Engineering. - Represent Prime Intellect at conferences, meetups, and on podcasts — and identify opportunities for the founding team to do the same. Content & Thought Leadership - Build a content engine spanning technical blog posts, research digests, case studies, video content, and social media. - Partner with Research to translate frontier work (Recursive Language Models, INTELLECT model series, CLaaS) into high-signal public content. - Develop a thought leadership program that positions Prime Intellect's founders and researchers as key voices in open AI infrastructure. Campaigns & Growth - Plan and execute product launches, research releases, and community events that generate developer awareness and adoption. - Run growth experiments across channels — organic social, paid, SEO, email, partnerships — and build measurement systems to track what works. - Own top-of-funnel metrics: developer signups, community engagement, content reach, and brand awareness. - Partner with Sales/GTM to ensure marketing-generated demand converts into pipeline and design-partner relationships. Marketing Operations - Build and manage the marketing tech stack (analytics, CMS, email, social scheduling, attribution). - Establish reporting cadence for marketing KPIs and campaign performance. - Manage external agencies, freelancers, and contractors as needed for design, video, and content production. What We're Looking For - 5+ years in marketing, developer relations, or developer marketing at a B2B infrastructure, open-source, or developer-tools company. - Deep technical fluency — you can credibly discuss GPUs, RL training loops, model architectures, and API design with engineers and researchers. - Proven track record building developer communities and running high-impact product launches. - Strong writer and storyteller who can produce or direct content that resonates with both technical and business audiences. - Analytical and experiment-driven — comfortable with attribution models, funnel metrics, and growth experiments. - Experience building small, high-performing teams from scratch in fast-moving environments. Bonus - Hands-on experience with AI/ML infrastructure, cloud platforms, or open-source ML tooling. - Existing presence and credibility in the AI research or infrastructure community (blog, Twitter/X, conference talks). - Experience with open-source community building and contributor growth programs. - Early-stage operator mindset — bias for building playbooks, not inheriting them. What We Offer - Cash Compensation Range of $150–300k + equity incentives - Flexible Work (remote or San Francisco) - Visa Sponsorship & relocation support - Professional Development budget - Team Off-sites & conference attendance - Opportunity to shape open AI infrastructure at Prime Intellect

• Lead and participate in novel research to build a massive scale synthetic data generation pipeline and orchestration solution • Optimize the performance, cost, and resource utilization of AI inference workloads by leveraging the most recent advances for compute & memory optimization techniques. • Contribute to the development of our open-source libraries and frameworks for synthetic data generation and distributed RL frameworks. • Publish research in top-tier AI conferences such as ICML & NeurIPS. • Distill highly technical project outcomes in layman approachable technical blogs to our customers and developers. • Stay up-to-date with the latest advancements in AI/ML infrastructure and tools, synthetic data gen research and proactively identify opportunities to enhance our platform's capabilities and user experience.

• Collaborate with leading experts in AI, distributed systems, and protocols to develop state-of-the-art open language models, coding agents, and scientific discovery models • Contribute to projects focused on democratizing AI and making it universally accessible through the Prime Intellect platform • Gain practical experience in developing and deploying large-scale AI models using novel architectures and distributed training techniques across thousands of GPUs and smaller clusters • Publish research papers and present findings at top-tier AI conferences • Develop a strong network within the decentralized AI community

• Build and optimize the systems infrastructure behind large-scale RL and distributed training workloads. • Improve end-to-end training efficiency across compute, memory, networking, and scheduling layers. • Design and implement low-level performance optimizations, including kernels, communication paths, and runtime improvements. • Work on distributed training systems spanning data, tensor, and pipeline parallel workloads. • Help shape the architecture of our RL training stack, including async rollout and post-training systems. • Contribute to open-source libraries and internal infrastructure used for frontier-scale model training. • Collaborate closely with researchers and infrastructure engineers to translate bottlenecks into concrete systems improvements. • Stay at the frontier of training systems, inference systems, compiler/runtime tooling, and hardware-aware optimization techniques.

• Design and build scalable RL training infrastructure — async trainers, environment orchestration, reward pipelines — across large GPU clusters. • Optimize performance, cost, and resource utilization of RL workloads using state-of-the-art compute and memory optimization techniques. • Contribute to our open-source libraries and frameworks for distributed RL training. • Publish research at top-tier venues (ICML, NeurIPS). • Write clear, approachable technical content distilling complex systems work for customers and the broader community. • Stay current with advances in RL systems, distributed training, and ML infrastructure, and proactively identify opportunities to enhance our platform.

• Design and implement interfaces across the platform covering compute orchestration and RL training • Translate complex backend systems into intuitive, production-ready product experiences • Own end-to-end product development from concept to deployment • Build and maintain a scalable design system with reusable components • Build for technical audiences: AI engineers and general software engineers • Convert RL concepts into usable visual primitives and interactive workflows • Implement platform developer tools including onboarding flows and debugging interfaces • Create seamless interactions between frameworks and platform UI