Applied AI: Product Strategy & Revenue Lead

Prime Intellect•San Francisco, CA

2d•Hybrid

About The Position

Prime Intellect is building the infrastructure that frontier AI labs build internally, and making it available to everyone. Our platform, Lab, unifies environments, evaluations, sandboxes, and high-performance training into a single full-stack system for post-training at frontier scale — from RL and SFT to tool use, agent workflows, and deployment. We validate everything by using it ourselves, training open state-of-the-art models on the same stack we put in customers’ hands. We are building for the next generation of AI companies, enterprises, and research teams that do not just want more GPUs. They want the ability to turn their own workflows, tools, data, and feedback loops into continuously improving models and agents. Prime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and a network of exceptional operators and founders across AI, infrastructure, and enterprise software, including leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, LangChain, Browserbase, Cloudflare, Sierra, Databricks, 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.

Requirements

Exceptional generalists with rare taste across AI, product, customers, and commercial strategy.
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.
Technical enough to earn trust with researchers and customers.
Commercially intense enough to close.
Strong product taste.

Nice To Haves

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.

Responsibilities

Help define how Prime Intellect turns frontier post-training infrastructure into a product customers can understand, buy, deploy, and expand.
Own the 'messy middle' by working 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.
Help 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.
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.
Turn vague, high-stakes customer conversations into clear technical and commercial strategy, identifying what the customer is trying to improve, the appropriate 'wedge' (compute, evals, environments, etc.), what Applied Research should build, what needs to be packaged as product, and the scope for POCs versus long-term deployments.
Shape Prime Intellect’s product motion before every part of the playbook is obvious by identifying patterns across customer conversations, building repeatable narratives, defining packaging, sharpening use cases, and distinguishing one-off noise from signs of a massive market.
Help answer questions about how to explain Lab to different customer segments, which customer workflows should become reference architectures, what to productize versus deliver as managed work, the strongest wedge for enterprise customers, signals for readiness for managed post-training, and how to turn Applied Research work into revenue without diluting the research agenda.
Own high-value customer opportunities from first serious conversation through qualification, scoping, proposal, POC, procurement, and expansion, measured by customer movement rather than activity.
Run discovery with technical and executive stakeholders, build the business case and technical wedge, own account strategy with leadership, draft proposals, scopes, and commercial structures, coordinate internal workstreams, create momentum through ambiguity, and turn early deployments into expansion and long-term platform revenue.
Work extremely closely with Applied Research, using technical taste to understand the viability of RL/post-training workflows and the potential of Applied Research prototypes to unlock deals.
Help Applied Research prioritize customer-facing work by bringing signal from the field regarding valuable evals, environments, agents, workflows, and technical demos.
Identify customer problems that are actually research problems in disguise.
Help write the playbook for category creation, contributing to positioning, sales narratives, customer decks, case studies, reference architectures, launch moments, and internal strategy, turning raw customer conversations into crisp language.
Invent the playbook for how frontier AI infrastructure gets built, packaged, sold, deployed, and scaled.