Founding Engineer

Origamics; San Francisco (CA); On-site; USD 180,000 - 300,000; Full time

Job summary

Origamics is hiring the founding engineers to join the founders in San Francisco, building the next generation of hardware design with AI models trained in-house. You’ll own architecture end-to-end, shape the stack, and drive CI/CD and tooling in a tiny, highly ambitious team.

This is not an early employee role; it’s founding-level responsibility with equity and impact. You'll work closely with a PhD-heavy research group to turn research into production-grade systems that customers trust with

Qualifications

• Experience designing scalable hardware-software stacks for complex systems.
• Ability to own architecture end-to-end from concept to production.
• Comfort working in a small founding team with high ownership.

Responsibilities

• Own product architecture, services and storage end-to-end with direct impact on production.
• Define tech stack and productivity tooling, shaping CI/CD and developer flow.
• Build integrations with engineering tools our customers rely on.
• Translate research models into production systems that engineers trust with real boards.
• Design internal engineering practices and workflows that scale a two-person team.

Skills

Distributed systems

Cloud infrastructure

CI/CD

Systems design

Stakeholder communication

Tools

CI/CD pipelines

Container orchestration

Job description

Origamics AI · Founding team · Full-time

We're hiring the first engineers to join the founders. Not employee #20. Not "early." Founding.

What we do

The technology used to design boards and electronics is some of the most complex in existence, and it hasn't changed much in decades. Engineers spend enormous amounts of time on setup, scripting, debugging failures, and waiting on runs that take hours or days. A single board program can drag on for 9 to 12 months, mostly because nobody catches a problem with the board until physical testing. By then it's late, and it's expensive to fix.

General-purpose AI and LLMs don't understand circuit physics, so we're building our own models in-house, from the ground up, trained specifically on how signals and fields actually behave on a board. We take schematics straight from the tools engineers already use, run our AI to generate fabrication-ready boards, and hand them back. No rip-and-replace, no throwing away twenty years of muscle memory.

Our vision is simple to say and hard to build: hardware design should move at the speed of software. Chips and boards aren't easy, but the tools around them just haven't kept pace with everything else.

We're a very small technical team by design. We've grown a company from pre-product to nine figures in annual revenue, and grown engineering orgs from 3 people to 50. We've published AI research with real citations behind it and hold multiple patents. We're now hiring the founding engineers on our team — not the fortieth — and you'd be building next to us from day one.

What you'll own

• The product architecture, services and storage solutions end-to-end, with nobody between your decision and production.

• Technical stack and productivity tools, selecting key vendors in our technical stack and contributing to the developer flow, from agentic systems to reliable CI processes.

• Integrations with engineering tools and services our customers already run, hardened enough that switching to us feels like nothing changed.

• Turning our research team's physics-informed models into production systems engineers trust with real boards.

• Design the agentic engineering practices this company runs on internally — the tooling and workflows that let a two-person team ship like a twenty-person team.

• Set the engineering bar for every hire that comes after you, because for a while, we will be the bar.

You might thrive if you...

• Have taken something from zero to one before and you want to do it again — this time with the equity, ownership, and title to match what you're actually doing.

• Understand the real trade-offs in storage, compute, and system architecture and optimize for the problem in hand vs the best theoretical solution.

• Have a good approach in build vs buy decisions and understand software maintenance costs, and scaling complexities.

• Have experience with — or are eager to grow into — agentic systems: dedicated containers, multi-agent orchestration, agent review, multi-model task distribution.

• Like the idea of getting fluent, fast, in building foundational models (not just wrappers) alongside a PhD-heavy research team, even though it's not your background yet.

Email [e-mail]. Tell us the hardest system you've built and why it mattered. We read every one of them.

* human generated em dash