Applied AI: Product Strategy & Revenue Lead

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.

About the job

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.

Skills

Product Strategy, Revenue Strategy, Artificial Intelligence, Post-Training, Reinforcement Learning, Supervised Fine-Tuning, Llm Evaluation, Agent Frameworks, Enterprise Ai, Infrastructure, Devtools, Procurement