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:
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What is the customer actually trying to improve?
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Is the wedge compute, evals, environments, sandboxes, managed RL, SFT, inference, or a full-stack workflow?
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What should Applied Research build or prototype?
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What needs to be packaged as product?
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What should be in scope for a POC versus a long-term deployment?
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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:
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How do we explain Lab to different customer segments?
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Which customer workflows should become reference architectures?
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What should we productize versus deliver as managed work?
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Where is the strongest wedge for enterprise customers?
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Which signals show that a customer is ready for managed post-training?
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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.
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Running discovery with technical and executive stakeholders
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Building the business case and technical wedge
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Owning account strategy with leadership
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Drafting proposals, scopes, and commercial structures
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Coordinating internal workstreams across Applied Research, Product, Engineering, Legal, and Finance
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Creating momentum through ambiguity
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Turning early deployments into expansion and long-term platform revenue
Applied Research Partnership
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:
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Which environments should we build?
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Which agents or workflows are most commercially valuable?
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Which technical demos will change the customer’s mind?
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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.