Founding Engineer

Get Hired. Work From Anywhere.•San Francisco, CA

5d•Onsite

About The Position

We're recruiting on behalf of a seed-stage, venture-backed software company building an AI-native platform in a large B2B category. The team is small, dense, and design-obsessed. They went from zero to real revenue within months of launching, count dozens of venture-backed startups and post-Series A companies as paying customers, and have raised over $5M from well-known institutional investors. The bar here is taste. If you've ever opened Linear, Raycast, or Vercel and thought "why doesn't every B2B tool feel like this?" — that's the standard you'd be hired to hold. This is a founding engineering hire. You'd work directly with the founders, ship to production every week, and have outsized influence on what the product becomes.

Requirements

0–4 years of engineering experience; new grads welcome
Strong full-stack ability across Next.js, React, Python, FastAPI, and Supabase
Degree or background in Computer Science, Data Science, or Industrial Engineering / Operations
Exceptional product intuition — you can articulate why a UI feels right or wrong and reason from first principles about user workflows
Ability to go from rough idea to shipped production feature without hand-holding
Comfortable owning customer calls and translating technical issues clearly in both directions
A portfolio, GitHub, or shipped side projects that demonstrate the taste bar above
Based in SF or able to relocate immediately; in-office 5 days a week
US citizen or existing valid work authorization

Nice To Haves

Prior founder, founding engineer, or early-stage startup experience
Experience with real-time collaborative interfaces, agent UIs, or eval/observability tooling
A public body of work: side projects, technical writing, or a GitHub history that shows fast iteration

Responsibilities

Core product surfaces. The workflows customers live in every day — configuration, orchestration, and the multi-step systems that run their operations.
The embedded AI agent layer. Agents that research, draft, and take action on the user's behalf, inside the product rather than bolted on.
Eval and observability tooling. The instrumentation that makes customers trust an AI to run something business-critical.
Taking features another engineer has built to 70% and finishing them to production-grade quality.
Direct customer contact. You'll sit in shared Slack channels with users, read their signals, and convert them into product decisions — no spec will be handed to you.
Product direction. Pairing with founders on what to build next. You're expected to contribute as much product intuition as engineering output.