ABOUT THE ROLE
Bardin is the always-on application engineer for the robotics and automation industry. We're a Physical AI company: the engineering judgment required to scope, deploy, and support complex automation, captured in machine-readable form for the first time.
We've spent a year proving this works. The product is live with customers. We know what breaks and what our users need. Now we're building the enterprise-ready version.
You'll build the interfaces our users work in every day. The role leans frontend but runs across the stack, sitting close to the AI to turn a powerful agent-and-graph backend into something people reach for. This is an engineering role, but the specs are thin by design. You'll make product calls in the course of building, and we'll expect you to have opinions about them.
WHO YOU'LL WORK WITH
You'll work with our CTO and join our founding dev team: a senior backend engineer, an AI engineer, a frontend developer, and an industrial applications engineer who came from the automation world and knows how our users actually work. Everyone owns their surface, and everyone makes judgement calls.
Small team. Short feedback loops. No layers between you and the decision.
IN THIS ROLE YOU'LL
- Own features end-to-end, from design through deployment.
- Build responsive UIs for real-time, agent-driven experiences: collaborative editing, streaming responses, and graph-backed answers rendered clear and actionable.
- Shape how people interact with the agents. The controls, feedback, and states that make an AI product feel trustworthy rather than opaque.
- Extend the APIs, services, and data models behind your features, and partner with AI, backend, and domain experts to ship them.
Requirements
- 4+ years building and shipping production web applications across frontend and backend.
- Strong with React and TypeScript, with a record building complex UIs.
- Strong with Next.js, Tailwind, and a modern component system (Radix, shadcn, or similar).
- Solid backend fundamentals: designing APIs, modeling data, reasoning about server-side behavior.
- Familiarity with real-time collaboration (WebSockets, Yjs) or rich-text editors (TipTap).
- Comfort in a monorepo (Turborepo, pnpm) and good testing habits (Vitest, Playwright).
- 1+ years integrating LLMs or AI APIs into production: streaming, tool calls, or RAG-backed interfaces.
- Good product instincts and sound judgment when the spec is thin.