Founding AI Engineer & Entrepreneur in Residence

Description

You join as the first engineer at a New York studio that builds AI-native companies for legacy industries, working directly with a founder who has done this before.

The studio picks one vertical, goes deep, and builds the product that industry should have had. It operates the companies itself rather than advising, and stays until they win.

The founder previously built a single-idea company in a legacy industry. It grew to hundreds of employees, tens of thousands of businesses on the platform, operations in several countries, and tens of millions raised.

Much of the real economy still runs on paper, fax, and one person who knows how everything works. Freight, rental yards, hangars, and substations generate trillions in revenue, and their software has barely moved in decades.

This is not a fit if you want a spec handed to you, have never talked to a customer and would rather not start, need a big team around you to be productive, or see a legacy industry as a step down rather than an opportunity.

Responsibilities

• Months 1 to 12: work as founding engineer. The founder and you will examine the chosen vertical, which you will learn about on the first call.
• Sit in on customer calls, then build version one with full ownership: data model, LLM and agent layer, evals, UI, and deployment.
• Ship to paying customers within the first quarter.
• Month 12 onward: become co-founder of the spinout, leading it as co-founder and technical lead. The studio provides capital, first customers, and operating support.
• Your equity sits in the spinout itself, not in a holding entity.
• Report directly to the founder with no one in between.
• Set the architecture and stack, and write the initial 10,000 lines of code.
• Build production LLM features that hold up for non-technical field users: retrieval, agents, structured extraction, evals, and guardrails.
• Turn old workflows and messy data, such as PDFs, spreadsheets, legacy ERPs, and phone calls, into inputs a model can act on.
• Talk to customers every week and let their feedback shape the product.
• Hire the first two or three engineers once the spinout has funding.
• Set the engineering culture for a company that does not exist yet.

Requirements

• At least 3 years as a software engineer, with real ownership of one product built from zero to one.
• Shipped production AI used by real customers, such as LLM apps, agents, RAG, or fine-tuning. You have debugged a model in production, not only in a demo.
• Working knowledge of Python or TypeScript, a modern web framework, Postgres, cloud infrastructure, and CI, and the ability to run the whole stack for a stretch on your own.
• Treat evals as a core engineering discipline, not an afterthought.
• Able to explain a technical decision to a mechanic, a dispatcher, or a CFO and have them agree.
• Time spent in a boring industry such as logistics, construction, aviation, energy, or industrial services. Even a summer job counts.
• Prior experience as a founder, founding engineer, or one of the first ten employees at a company.
• Have shipped something people paid for, built on your own.
• Strong, experience-based opinions about tooling.
• Live in New York or be willing to move there now. The work is in person.

Conditions

Work is in person in New York.

Hiring process: reply within 48 hours, then a 30-minute call with the founder, then a paid half-day working session on a real problem in New York, then dinner, then an offer. If it is a good fit, the whole process takes about two weeks.

The studio is an equal opportunity employer and hires based on what you have built and how you think.

Benefits

• Market-rate salary from day one. This is not a pitch built on low pay and equity.
• Co-founder equity in the spinout, vesting over four years with a one-year cliff.
• Work alongside a founder who has already scaled this kind of company, as a hands-on partner rather than a board member.
• Capital and distribution already arranged for the first product.
• A seat in New York on a small team that prefers building to discussing.