M
Generative AI Engineer
Marcus Henley
Full-time · Mid-level · Los_Angeles
About the role
We got into YC six months ago. We launched three months ago. We have 11,000 users and $0 in VC funding beyond our YC check, which means every decision we make has to be a good one. Here's what we're building: a legal document assistant that helps small business owners understand contracts without paying $400/hour for a lawyer. Our early users love it. Our current stack is OpenAI + LangChain + a retrieval layer that's frankly too slow and too expensive to scale. That's the problem we need you to help solve. We're not looking for someone to maintain what we have. We're looking for someone who will look at our architecture and tell us — honestly, specifically — what's wrong with it and what to do instead. We're two engineers and one designer. You'd be the third engineer. You'd have real ownership. You'd work on problems that matter to people who genuinely can't afford alternatives. If that sounds like a good way to spend the next year or two, let's talk.
Responsibilities
- Audit and rebuild our document retrieval pipeline to reduce latency and per-query cost by at least 40%
- Implement chunking, embedding, and re-ranking strategies appropriate to legal document structure
- Own prompt architecture end-to-end: system design, A/B testing, and evaluation
- Help define our technical roadmap and make the case for or against architectural decisions in writing
- Work directly with users to understand failure modes and translate them into engineering improvements
Requirements
- 3+ years of software engineering with at least 1 year building LLM-powered features in production
- Hands-on experience with LangChain, LlamaIndex, or equivalent RAG frameworks — not just tutorials
- Strong Python: async patterns, clean architecture, you can write code other people maintain
- OpenAI and Anthropic API experience including tool use, function calling, and streaming
- Vector databases in production: Pinecone, Weaviate, Qdrant, or similar
- A genuine point of view on what makes RAG systems fail at scale
Benefits
- Real equity in a company with paying users and genuine traction — not a paper promise
- Full remote, async-first culture, no mandatory stand-ups
- $105,000 – $125,000 base salary
- $1,000 equipment stipend on day one
- You will have a meaningful say in what we build and how we build it
Job Type
Full-time
Level
Mid-level
Language
English
Salary Range
$105,000 – $125,000
AI Expertise
NLP & Prompt Engineering
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