# Applied AI: Product Strategy & Revenue Lead

**Company:** [Prime Intellect](https://hotfix.jobs/companies/prime-intellect)
**Location:** San Francisco, CA
**Role:** GTM Engineering
**Skills:** Product Strategy, Revenue Strategy, Artificial Intelligence, Post-Training, Reinforcement Learning, Supervised Fine-Tuning, Llm Evaluation, Agent Frameworks, Enterprise Ai, Infrastructure, Devtools, Procurement
**Posted:** 2026-07-08

> Own the intersection of customer discovery, AI product strategy, and revenue for a frontier post-training infrastructure platform. The role requires strong technical judgment, commercial execution, excellent communication, and comfort shaping a category before the product motion is fully defined.

## Job Description

## Responsibilities
- Translate customer needs into product strategy, technical scope, commercial proposals, and revenue paths.
- Work with frontier AI labs, startups, and enterprise AI teams to understand workflows, infrastructure gaps, and post-training needs.
- Identify product wedges across compute, evaluations, environments, sandboxes, managed RL, SFT, inference, and full-stack workflows.
- Shape pre-PMF product motion, including customer narratives, packaging, use cases, reference architectures, and managed-work decisions.
- Own high-value opportunities from discovery and qualification through scoping, proposals, POCs, procurement, expansion, and long-term platform revenue.
- Run discovery with technical and executive stakeholders; build business cases and account strategies.
- Coordinate Applied Research, Product, Engineering, Legal, and Finance workstreams.
- Bring customer insight to Applied Research prioritization and identify commercially valuable prototypes, evaluations, environments, agents, and workflows.
- Contribute to positioning, sales narratives, customer decks, case studies, reference architectures, launches, and internal strategy.

## Requirements
- Strong product and commercial judgment.
- Ability to quickly understand technical products and earn trust with researchers and customers.
- Excellent written communication and ability to create customer-facing decks, memos, proposals, and launch narratives.
- High agency and comfort with ambiguity.
- Strong customer-problem judgment and ability to create structure where none exists.
- Understanding of enterprise buying, proofs of concept, procurement, and expansion.
- Strong interest in AI, post-training, agents, evaluations, and infrastructure.
- Ability to collaborate with researchers, engineers, executives, and operators.
- Commercial intensity sufficient to close opportunities.

## Nice-to-haves
- Experience with reinforcement learning, supervised fine-tuning, evaluations, agent frameworks, or LLM post-training.
- Experience selling or deploying infrastructure, AI platforms, developer tools, or enterprise AI products.
- Experience with frontier AI labs, model companies, or infrastructure-heavy startups.
- Network across AI startups, research labs, or enterprise AI teams.
- Founder mentality and willingness to perform hands-on work in an ambiguous environment.

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