Applied AI Product Strategy & Revenue Lead

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 â 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.

The Role

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 â 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.

What Youâll Own

Customer-to-Product Translation

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?

Pre-PMF Product Strategy

You will help shape Prime Intellectâs 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?

Revenue Ownership

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 meas

Applied AI Product Strategy & Revenue Lead at Prime Intellect