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# Founding Engineer

- **Company:** Buoyant, Inc.
- **Location:** San Francisco, CA, United States
- **Contract:** Permanent contract
- **Skills:** Microsoft Word, Artificial Intelligence, Information Extraction, Information Retrieval, PostgreSQL, Machine Learning, Language Modeling, Parsing, Next.js, Software Engineering, Structured Text, TypeScript, Web Applications, Network Routers, ReactJS, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Firebase, Pgvector, AI Platforms, Virtual Agents
- **Published:** November 7, 2026
- **Apply:** https://www.juju.com/job/28_25197_92ff47d4baacfbe8cd202d42559d6222

## About the Role

* Strong TypeScript - the entire codebase is TypeScript. You're comfortable with strict typing, immutable data, and functional patterns.
 * React experience - both the web app (Next.js App Router) and Word add-in are React. Server components, hooks, state.
 * LLM application development - not ML research, but building reliable systems on top of language models. Prompt engineering, structured extraction, RAG, eval.
 * PostgreSQL - schema design, migrations, RLS. pgvector is a bonus.
 * A bias toward shipping - you care what users experience, not just what's technically elegant., * Office.js or Word add-in experience (rare and valuable).
 * Document processing - parsing PDFs, DOCX, and other formats into structured text.
 * Supabase / Firebase / BaaS experience - understanding tradeoffs.
 * Prior startup experience - comfort with ambiguity, fast iteration, wearing multiple hats.

## Description

Full-stack product engineer who helps us build the AI platform for engineering consulting firms - agents that own entire professional workflows, starting with proposals.

Buoyant is building the AI layer for engineering consulting firms - AEC, water, transportation, environmental. Our bet is that AI can own the dozen knowledge-heavy workflows these firms run every week: proposals, project kickoff, QA/QC, compliance filings, client communications. Anything that isn't the engineering itself is work an agent should do.

Today's product is an AI agent that drafts proposals. It reads the RFP, researches the firm's past work, assembles a team, builds a strategy, and writes - grounded in the firm's real project history, in their voice. It lives inside Microsoft Word because that's where proposal teams already work. It's in production with customers who are winning work with it.

We're backed by $1M+ from Silicon Valley investors focused on AEC and climate. Proposals are the beachhead; what comes next is every other workflow these firms run. The founding engineer is joining at the point where the first agent has proven out and we're about to extend horizontally., Firms have decades of documents - thousands of PDFs, resumes, past proposals, specifications. An agent writing about stormwater design in urban California needs the right ten of those, not the thousand closest matches. Better chunking, hybrid retrieval, re-ranking, per-firm learning - this is where the product wins or loses.