Founding Forward Deployed Engineer

About the role

We're a stealth holding company acquiring and operating software businesses in overlooked, mission-critical industries - the systems that run the physical economy but have been ignored by Silicon Valley. We're rebuilding them as AI-native from the inside out. We're small, well-capitalized, and moving fast. This is not a research lab. This is a build shop with real customers, real revenue, and real P&Ls.

Responsibilities

• Building the industry-specific GPT: the reasoning layer that encodes the domain expertise of each vertical we operate in.
• Embedding AI-native functionality directly into our AI-native system of record: not bolt-on chatbots. Core workflow replacement. Agents that do the job.
• Moving legacy systems of record datasets into new ai-native ones: Core workflow replacement. Agents that do the job.
• Making it repeatable: Every new company we bring into the portfolio should benefit from the playbook, model stack, eval harness, and infra you build.
• Managing AI talent across our operating companies: you'll be a player-coach for the AI engineers embedded at each sub. Split: 90% building. 10% managing.
• Ship a working domain-specific agent into a live customer environment in your first 30 days
• Build the eval infrastructure that lets us measure whether these agents are actually replacing human workflows (not vibes)
• Fine-tune, distill, or route across frontier + open models depending on the economics of each vertical
• Sit with customers. Watch them work. Instrument their workflows. Rebuild them.
• Write the AI engineering playbook that every future portfolio company inherits on day one

Requirements

• You are a builder first. You've shipped things people actually use. Alone or as the clear technical lead.
• You are a forward-deployed engineer. You'll fly to a customer site, sit in their office, and understand the business before you write a line of code.
• You've been called a 10x engineer and it wasn't in a self-review.
• You have strong opinions about when to fine-tune vs. prompt vs. RAG vs. agent vs harness and you can defend them with numbers.
• You've built with the current frontier stack (Claude, GPT, Gemini, open models) in production, not just in a Jupyter notebook.
• You are allergic to slide decks and comfortable with ambiguity, small teams, and unglamorous industries.