Original listing text, shown exactly as published by the company.
About the role
You'll join our AI Engineering team as a Senior Engineer embedded in one of our product pods, reporting to our AI Engineering Lead. You'll own AI features for grant discovery end to end, from the data backbone that crawls and structures messy source data, through the RAG and agentic systems that match grants to nonprofits, to production deployment and ongoing evaluation. It's a hands-on, high-ownership seat with direct access to founders and real room to grow as the engineering team scales!
What you will do
Build agentic AI systems and ship them to production
- Build tool-using LLM systems that plan, call tools, and run multi-step workflows for tasks like grant discovery, data ingestion, and research assistance.
- Build agentic data-processing pipelines that crawl the web, pull and dedupe messy source data at scale, and structure it into clean, queryable databases other teams build on.
- Turn prototypes into resilient production services with clear fallback, cost, and latency budgets.
Own RAG and ranking end to end
- Own RAG end to end: ingestion, chunking and embedding strategy, hybrid retrieval, re-ranking, citations, and grounding.
- Build ranking and scoring systems that match grants to nonprofits, universities, and foundations using complex relevance techniques.
- Continuously improve recall and precision and keep indices healthy as the dataset grows.
Ship safely and raise the bar
- Stand up evaluation and observability so our AI is grounded, safe, and cost-effective, and treat LLM behavior as non-deterministic by design rather than as a regular API.
- Partner directly with founders and your pod on undefined, complex problems with real autonomy.
- Write clear, maintainable, well-tested code and build reusably so your work expands across teams.
What we're looking for
Required
- 7+ years of professional software engineering experience, with deep, recent, multi-year Python and strong relational database and schema design skills.
- Solid CS fundamentals and a demonstrated track record of owning complex systems end to end, from design through production reliability.
- At least 1 year of hands-on experience building with modern LLMs (as an IC).
Nice to have
- Real RAG depth: hybrid search (keyword plus vector), re-ranking or fusion methods, and grounded citations, tuned in production rather than read about.
- Hands-on with at least one of LangChain, LangGraph, or LlamaIndex.
- Vector databases beyond pgvector (Pinecone, Qdrant, Milvus).
- Built end-to-end agentic data-processing systems (crawling, dedup, structuring) with whole-system ownership.
- Evaluation and observability for AI systems: golden datasets, precision vs. recall, LLM-as-judge, and drift monitoring.
- Ruby on Rails (our core platform is on Rails), deep SQL, and experience with AWS or GCP, Docker, and CI/CD.
- Startup experience and comfort operating in fast, scrappy, low-process environments.
