About the Role
the company and dbt are building the open data infrastructure that powers AI agents you can trust.
the company is looking for a Staff R&D Software Engineer to join our fast‑growing the company AI team. Your data stack was built for humans — but agents are the new primary data consumers, and they have fundamentally different requirements. Agents can’t intuit context; it must be explicitly codified, governed, and traceable. We’re building the governed context layer that solves this problem: Agents Schema, an open standard for storing agent‑ready context directly in the customer’s own data warehouse, and Context Builder, the managed service that keeps it filled and fresh.
This role goes well beyond standard engineering. You’ll research emerging techniques in the fast‑moving AI landscape and bring real product and market understanding to decide which ideas are worth pursuing — and then you’ll take what you’ve learned and ship it as production software. We’re looking for a true generalist who is willing and able to wear whatever hat the moment calls for: prototyping a new retrieval technique one week, hardening a backend service the next, then doing SRE or QA work when the team needs it. At this level, you’re expected to be a trusted expert beyond your own team — defining technical direction that other teams build on, taking high‑level direction and turning it into concrete plans with a high degree of independence, and using sound judgment to decide what deserves your attention. the company AI operates like a startup within the company, and we need engineers who thrive on that range and ambiguity rather than staying in one lane.
the company is the epitome of data‑driven development — our engineering team is focused on building a world class product that:
- Builds Infrastructure Agents Can Trust — join our mission to deliver the governed context layer that AI agents depend on: accurate semantic definitions, traceable lineage, data contracts, and auditable history baked in from the start.
- Embraces Open Standards — help build portable, interoperable data infrastructure: Agents Schema, open formats (Iceberg, Delta Lake), MCP‑native interfaces, and connector skills that work with any model and any compute.
- Scales Without Breaking — work to make the company AI efficient at agent scale, where unit costs deflate as volume grows and context retrieval is fast, accurate, and cost‑controlled.
We emphasize using no‑nonsense tools and take great pride in the simplicity and effectiveness of the systems we build. Our back‑end is built on Java, Python, Postgres, and Kubernetes, and our front‑end is built on React and TypeScript.
This is a full‑time hybrid position based out of our Austin, TX office. Our hybrid work model offers a blend of remote flexibility and in‑person collaboration, including two days in the office each week to connect and build as a team.
Technologies You’ll Use
Python, Java, SQL, dbt, LLMs (Claude, ChatGPT, Gemini), vector databases, BigQuery / Snowflake / Databricks, MCP protocol, React, TypeScript, Kubernetes
What You’ll Do
- Research emerging techniques in retrieval, reasoning, and agentic AI, and decide what’s actually worth pursuing for the company AI’s roadmap — then convince others
- Prototype new ideas quickly, then take the ones that prove out and turn them into shipped, production‑grade features
- Define technical direction that spans multiple teams within the company AI, ensuring architecture decisions made in one area don’t create problems in another
- Build and maintain both back‑end and front‑end systems for the the company AI product — from