What we're looking for
We're looking for an AI engineer who owns a business outcome end to end: deciding what to build, building it, and proving it worked. Engineers take a company goal, find where the leverage is, turn it into a roadmap, ship, and measure whether it moved the number.
Our product runs on agents. They decide what work needs to happen for each customer, do a lot of that work autonomously, check the quality of what got done, and assist the people on our team who handle the rest. You'll own that layer.
Concretely, you'll build agentic systems that carry multi-step work from start to finish, the evaluation infrastructure that tells us whether they're any good, the retrieval layer that gives them accurate knowledge to reason over, and the AI features our internal users rely on every day. The surface is wide and still being defined, so there's a lot of room to shape what gets built.
We don't have a strong opinion about which corner of AI engineering you came from — agents, RAG, evals, fine-tuning, retrieval. What matters is that you're genuinely fluent with modern LLMs, that you treat them as a backend you can build reliable systems on, and that you've shipped something real to real users.
Qualifications
- You've built LLM-powered systems or agents, shipped them to production, and kept them reliable — not demos, but systems real people depend on.
- Strong overall AI fluency. You can reason concretely about prompting, tool use, context management, retrieval, latency, cost, and failure modes.
- You've built evaluation into your work – designed metrics, captured signal from production, and used it to systematically improve quality.
- You have product judgment, and can show it: a time you challenged a spec, cut scope to ship what mattered, changed course after watching real users, or built something nobody asked for that turned out to matter.
- Solid software engineering fundamentals. You write code others can maintain, and you own what you ship.