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San Francisco, California, United States
Onsite
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.
This is not a traditional sales role. It is not a traditional product role. It is not a traditional solutions engineering role.
You will help define how Prime Intellect turns frontier post-training infrastructure into a product customers can understand, buy, deploy, and expand.
Today, the hardest part of the business is not selling raw compute. It is refining the product, customer motion, and technical wedge together with Applied Research, Product, Engineering, and the customer. We are selling something much more complex and much more valuable than GPUs: the ability for customers to build their own lab — environments, evals, verifiers, agents, training runs, and deployment loops that compound over time.
You will own that messy middle.
You will work directly with customers, the CEO, GTM leadership, Applied Research, and Engineering to translate ambiguous customer pain into a concrete product strategy, technical scope, commercial proposal, and path to revenue. You will help us figure out where the product is ready, where it needs to be shaped, what the customer actually wants, and how to turn early traction into repeatable motion.
This is a role for someone who wants to be in the room where a new category is being created.
You will work with frontier AI labs, fast-growing AI startups, and enterprise AI teams to understand what they are trying to build, where their current stack breaks, and how Prime Intellect can become the infrastructure layer underneath their post-training and agent workflows.
You will turn vague, high-stakes customer conversations into clear technical and commercial strategy:
What is the customer actually trying to improve?
Is the wedge compute, evals, environments, sandboxes, managed RL, SFT, inference, or a full-stack workflow?
What should Applied Research build or prototype?
What needs to be packaged as product?
What should be in scope for a POC versus a long-term deployment?
What is the fastest path to a strong yes?
You will help shape Prime Intellect’s product motion before every part of the playbook is obvious.
That means identifying patterns across customer conversations, building repeatable narratives, defining packaging, sharpening use cases, and helping the team understand which customer asks are one-off noise versus signs of a massive market.
You will help answer questions like:
How do we explain Lab to different customer segments?
Which customer workflows should become reference architectures?
What should we productize versus deliver as managed work?
Where is the strongest wedge for enterprise customers?
Which signals show that a customer is ready for managed post-training?
How do we turn Applied Research work into revenue without diluting the research agenda?
You will own high-value customer opportunities from first serious conversation through qualification, scoping, proposal, POC, procurement, and expansion.
You will not be measured on activity. You will be measured on whether the most important customers move.
This includes:
Running discovery with technical and executive stakeholders
Building the business case and technical wedge
Owning account strategy with leadership
Drafting proposals, scopes, and commercial structures
Coordinating internal workstreams across Applied Research, Product, Engineering, Legal, and Finance
Creating momentum through ambiguity
Turning early deployments into expansion and long-term platform revenue
You will work extremely closely with Applied Research.
The best version of this role has enough technical taste to understand where an RL/post-training workflow is real, where a customer is hand-waving, and where a sharp Applied Research prototype could unlock a major deal.
You will help Applied Research prioritize customer-facing work by bringing signal from the field:
Which evals matter?
Which environments should we build?
Which agents or workflows are most commercially valuable?
Which technical demos will change the customer’s mind?
Which customer problems are actually research problems in disguise?
The market understands compute. It does not yet fully understand full-stack post-training infrastructure.
You will help write the playbook.
You will contribute to positioning, sales narratives, customer decks, case studies, reference architectures, launch moments, and internal strategy. You should be able to turn raw customer conversations into crisp language the entire company can use.
We are looking for exceptional generalists with rare taste across AI, product, customers, and commercial strategy.
You might come from:
Technical GTM at a frontier AI, infra, devtools, or enterprise software company
Founder or early operator experience at an AI startup
Product or strategy at a highly technical company
Forward-deployed engineering, solutions, or applied AI work
Investing, venture, or strategic finance with deep AI infrastructure exposure
Research-adjacent roles where you worked directly with customers or product teams
You should have:
Strong product and commercial judgment
Ability to understand technical products quickly
Excellent written communication
High agency and comfort with ambiguity
Taste for what makes a customer problem real
Ability to work with researchers, engineers, executives, and operators
Sharp instincts around enterprise buying, POCs, procurement, and expansion
Obsession with AI, post-training, agents, evals, and infrastructure
Ability to create structure where none exists
You do not need to be a researcher, but you should be technical enough to earn trust with researchers and customers.
You do not need to be a traditional salesperson, but you should be commercially intense enough to close.
You do not need to be a PM, but you should have strong product taste.
Experience with RL, SFT, evals, agent frameworks, or LLM post-training
Experience selling or deploying infrastructure, AI platforms, devtools, or enterprise AI products
Experience working with frontier AI labs, model companies, or infra-heavy startups
Ability to write excellent customer-facing decks, memos, proposals, and launch narratives
Strong network across AI startups, research labs, or enterprise AI teams
Founder mentality and willingness to do unglamorous work to win
Most GTM roles ask you to sell a product someone else already defined.
This role asks you to help define the product, the market, the motion, and the revenue engine at the same time.
You will work on the hardest commercial and product questions at one of the fastest-growing companies in AI infrastructure. You will sit close to customers building real AI systems, researchers pushing the frontier of post-training, and leadership making company-defining decisions.
If you want a clean playbook, this is not the role.
If you want to help invent the playbook for how frontier AI infrastructure gets built, packaged, sold, deployed, and scaled, this is the role.
Competitive cash compensation and meaningful equity
Flexible work in San Francisco or hybrid-remote
Visa sponsorship and relocation support
Professional development budget
Team off-sites and conference attendance
A front-row seat to building the infrastructure layer for open AI
Apply to help Prime Intellect turn frontier post-training infrastructure into the product, platform, and customer motion that powers the next generation of AI systems.