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Prime Intellect

Open frontier-model stack for enterprise RL

Product Marketing Lead

Product MarketingConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USAIn-person

About the Job

Requirements

  • At least 8 years of product marketing or closely related experience, including substantial experience marketing technical products built for developers, engineers, or AI teams.
  • Experience taking a product or product marketing function from zero to one, building positioning, launches, and sales materials while the audience, category, or playbook was still being defined.
  • A track record supporting a sales-led motion and working directly with sellers and technical partners on real opportunities, with messaging and materials that helped customers make a buying decision.
  • Customer research experience demonstrating how customer conversations changed positioning, challenged assumptions, or led to a different use case focus.
  • Technical fluency and curiosity to learn complex products, explain how they work, and earn credibility with developers and researchers while making product value clear to business buyers.
  • Strong writing and editorial judgment, with the ability to turn dense technical discussions into clear stories without losing what makes a product distinctive.
  • Demonstrated hands-on use of AI tools and agents in work.
  • Comfort with ambiguity and a bias toward shipping, including choosing priorities, producing a strong first version, and improving it as evidence comes in.

Responsibilities

  • Define and evolve product positioning and category narrative, helping customers understand open post-training infrastructure, its value, and Prime Intellect's role as a partner.
  • Translate technical capabilities into clear messaging for developers, AI teams, and buying decision-makers; build the core narrative for the Open SuperIntelligence Stack and Model Factory through product pages and use-case stories.
  • Build a regular practice of customer interviews, sales-call participation, and win/loss analysis to learn customer problems, buying triggers, and gaps in the product story.
  • Test and revise messaging based on customer feedback and opportunity progress; share insights with Product, Engineering, and Applied Research to sharpen target use cases, inform packaging, and distinguish repeatable needs from one-off requests.
  • Own product launches from positioning and audience selection through launch content, seller readiness, and follow-through; work with Marketing and Sales to drive relevant customer conversations and qualified opportunities.
  • Build sales materials including pitch decks, demos with technical partners, competitive guidance, objection handling, and customer proof; participate in live opportunities as useful and improve materials based on customer responses.
  • Develop early customer work into case studies, references, and clear accounts of customer outcomes; build design-partner relationships that strengthen the product story and feed learning back into the product.
  • Establish initial product marketing processes and priorities, and measure whether the work helps customers understand the product, progress through evaluation, buy, and expand their use.
  • Use AI tools and agents to accelerate research, synthesis, and production while maintaining accuracy and craft.

Desired Qualifications

  • Experience with AI or machine-learning infrastructure, post-training, reinforcement learning, evaluations, or agent platforms.
  • Experience helping define an emerging category or repositioning a technical product as the product and market evolved.
  • A technical background, hands-on product experience, or experience building a customer advisory or design-partner program.

About the Company

Prime Intellect

Open frontier-model stack for enterprise RL

Prime Intellect provides an open, end-to-end superintelligence stack for enterprises to train and continually improve their own AI agents. The platform includes scalable compute, large-scale reinforcement learning, environments, sandboxes, evaluations, and deployment tools, and it ships open frontier models to customers. It covers the full lifecycle from training to deployment plus post-training support, enabling agentic training at scale. Its differentiator is the open, end-to-end stack used across thousands of customers with strong backing from top investors, and its goal is to help organizations build and iteratively improve their AI agents using open frontier models.

Company size
51-200
Company stage
Series A
Total funding
$150.5M
Headquarters
San Francisco, CA
Founded
2024

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