Founding Applied AI Engineer
Gabriel Talent
About the Company
A stealth, seed-stage startup building AI tools for engineers at complex manufacturers, the AI-native equivalent of modern coding assistants, but for the people who design physical products like vehicles and aircraft, work still largely coordinated across teams via spreadsheets today. Backed by well-known institutional investors with a notable advisory bench spanning aerospace, automotive, and regulatory backgrounds. The company already has signed commercial deals with some of the world's largest manufacturers.
About the Role
This is the company's 4th engineering hire, joining a tight three-person team. It's a founding, high-ownership seat with two areas of focus, ideally you're strong in both, with priority set by where your strengths lie. First, helping scale and run a large, active customer pilot with a major manufacturer. Second, and the current priority, a new product direction: building models of how a complex system's hardware, software, mechanical, electrical, fluidic, and thermal subsystems connect, real engineering work, not something you can just point a general model at. There's some infrastructure work too, including limited on-premise deployment for sensitive customers, but this is explicitly not an infra-heavy seat, product comes first, and you need to be genuinely interested in working directly with customers to figure out what to build.
What You'll Own
- A new product direction: system modeling of complex, multi-physics engineering systems, from design and requirements data
- Helping run and scale a large active customer pilot
- Some infrastructure work, kept deliberately lean
- Direct customer work from day one, including travel to shape the product
- Eventually, training custom models for customers
What We're Looking For
- Elite raw ability with a genuine spike, something you're exceptional at, not just a flat, solid generalist profile
- Strong engineering judgment paired with real product sense: the ability to translate customer input into what's actually valuable and buildable
- Founder-level thinking on trade-offs (ship fast with debt versus slower but right)
- Roughly 3 to 5 years of experience is typical, though genuinely exceptional junior or more senior candidates are welcome
- Ex-founder or real startup experience is a strong signal; an all-big-tech background alone often signals lower risk tolerance for this kind of role
- A hardware background helps but isn't required, genuine interest in physical AI and engineering matters more
- High trust, low ego, collaborative, and committed through the messy early stage of a company
Logistics
San Francisco, in-person. Compensation is $180K to $250K plus 0.1% to 0.7% equity. Visa sponsorship is available, and relocation is supported for candidates worldwide.