Software Engineer, AI

About the role

About Ande

Ande makes Corporate Entertainment Effortless. Marketing, sales, EAs and workplace teams use Ande’s AI to book client dinners, sports suites, catering, merch and more. Policies, spend and logistics are managed in one place, with one vendor.

Over 60 enterprises run their entertainment on Ande, with $400 million+ in annual entertainment spend flowing through a network of 93,000 entertainment providers across 90+ cities. Ande has raised $52 million+ in seed and Series A funding from Lightspeed Venture Partners, Redpoint Ventures, Duration Ventures and Sierra Ventures, with participation from Bain Capital Ventures.

About the Role

You will own the intelligence behind the Ande Agent. That's the concierge and its team of subagents that take a request from one sentence ("dinner for 12 with the Acme team next Thursday, somewhere private") to a confirmed, paid, on-policy booking. Along the way they source venues, compare options, email the venue and negotiate terms, and handle every change after that.

This is not a chatbot or a demo. The agent acts on real money, on behalf of real executives, with real venues on the other end of the thread. It runs in production today for reservations and private dining. Our strategy depends on carrying more of the work with every request: more kinds of entertainment, more cities, more of the workflow, without growing headcount to match. You will set how we get there.

What You'll Do

- Own the agent system. Ande runs on a root concierge agent with specialist subagents for sourcing, comparison, and venue coordination, on a self-hosted agent harness behind queued turn workers. You’ll own its architecture, model choices, and upgrade path, and make sure it holds up under latency budgets, provider failover, and real-world load.

- Build tools the agent can trust. The agent has about 75 typed capabilities that let it search, hold, book, pay, and message. Each one runs as the acting user and respects who they’re acting for, such as an assistant booking on behalf of an executive. Consequential actions become proposals that the booker confirms. You’ll decide what the agent is allowed to do, and how to make it safe.

- Make evals the gate for every change. You’ll build scenario suites, graders, and LLM judges calibrated against human labels. They replay from recorded runs on every PR and run in full nightly. Your goal is evals trustworthy enough that a model upgrade or prompt change can ship on the strength of the number alone, and cheap enough to run all the time.

- Power venue correspondence. Our agent negotiates with venues over email. It reads replies, pulls out offers, minimums, deadlines, and payment requests, and knows when to push, when to wait, and when to hand off to a person. You’ll make sure all of it is resistant to anything a third party writes into a thread.

- Build memory and the supply learning loop. You’ll shape what the agent remembers about a company, its people, and its venues, with clear rules on what’s shared and what stays private. You’ll also build the loop that turns every venue conversation into structured supply data, like which room actually fits 14 or what a venue will do on a Tuesday, so each recommendation is better than the last.