Founding Engineer, AI Procurement Platform

On-site (San Francisco, CA) · $160K–$180K · 3 openings

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**About the company**

An early-stage, seed-funded enterprise AI startup building AI agents for direct procurement — the sourcing, supplier management, and supply-chain-risk work that large manufacturers and enterprises run on every day. Despite being newly incorporated, the company is already closing large enterprise contracts and is backed by experienced operators and investors with a track record in category-defining companies. This is an underserved, unglamorous corner of enterprise software with real, immediate demand and very little modern competition.

**The role**

You'll build the systems that let enterprise teams trust an AI agent with genuinely high-stakes procurement decisions — structuring large volumes of procurement data, owning integrations with enterprise systems, and shipping workflows customers use daily. This is a full-stack seat that leans backend, and it's genuinely end-to-end: you'll own features from the initial customer conversation through deployment and iteration, not just write code to someone else's spec. This is also a founding-stage hire in the truest sense — you'll help set engineering practices for the team that comes after you, and you're expected to grow into leading it.

**What you'll do**

- Build and ship across frontend, backend, data infrastructure, and deployment

- Own features end-to-end, from the initial customer conversation through deployment and iteration

- Develop reliable agent workflows, evaluations, and AI/ML pipelines, including the guardrails that make agent actions safe and auditable

- Design data schemas for complex procurement, manufacturing, and enterprise resource data

- Own an enterprise customer relationship end to end — understanding their needs, servicing the account, and proving real value

- Help establish the engineering practices that shape the team as it grows, and contribute directly to the product roadmap

**What you must bring**

- Full-stack engineering ability that leans backend — you can take a spec and build it end-to-end to near-production quality

- Fluency in at least one of Python, TypeScript, Java, or C++, plus a modern backend framework

- Strong computer science fundamentals: scalable backend systems, databases, distributed architecture, and cloud deployment experience

- A real, working understanding of LLMs, agents, tool use, and evaluation — strong ML backgrounds are also welcome here

- High ownership and low ego, with genuine comfort receiving direct feedback

- A demonstrated habit of building things without being asked — open source work, research systems, or products with real users

- Genuine ambition to grow into leading a team, not just execute tickets indefinitely

- Willingness to learn an unfamiliar industry (procurement, supply chain, ERP, manufacturing)

- Comfort working in person, full-time, in a small, high-intensity team

**Nice to have**

- A competitive programming, math olympiad, or quantitative trading background (this is the archetype the team has found strongest here)

- Prior founder experience where the company didn't work out (wrong idea, co-founder split, couldn't raise) — this is treated as a real risk-taking signal, not a red flag

- A strong eye for product design alongside backend depth

- Direct experience with agent/LLM evaluation frameworks and safety guardrails

**Compensation & logistics**

- $160K–$180K base salary plus 0.5%–1% equity

- Benefits include DoorDash credit, gym, a full home workstation, and insurance

- Fully in-person in San Francisco

- 3 openings

- Not open to any visa sponsorship — candidates must already be authorized to work in the US

**This isn't the right fit if**

- You're front-end only or genuinely weak on backend and systems work

- You wait to be assigned work rather than building things unprompted

- You have a track record of short, repeated stints (three months or less) at multiple companies

- You're looking for a large, established team or a fully built-out playbook to follow

- Your technical credibility rests mainly on credentials rather than demonstrated, hands-on ability