About the role
About Basis Basis builds real agents that do real work in the real economy. Our agents operate for hours at a time, performing end-to-end work for some of the largest accounting firms in the world. We recently raised $100M at >$1B valuation and are racing to deploy the most advanced applied ML at production scale. Our investors include: Khosla Ventures (Keith Rabois & Vinod Khosla), Accel (Miles Clements), Google Ventures, Nat Friedman & Daniel Gross, Adam D'Angelo, Jeff Dean, Jack Altman, Noam Brown, Kyle Vogt, Amjad Masad, Clem Delangue and many other operators/technical leaders. "Basis is on the frontier of building production-grade, long-horizon agents. They've pushed the limits of what we thought our models could do on real-world, economically valuable, complex accounting tasks. They've been a great collaborator in helping us shape what the future of agents looks like." — Prashant Mital, Applied AI Lead, OpenAI
About
the Team Accounting post-Basis looks different. You will be part of a specialized team that will lead the charge into this new reality by building our core product. While you won't be doing accounting for clients in this role, you will work closely with both Basis' customers and our engineering team to build a product which will transform accounting workflows. The key to success on this team is being able to see around corners and think about how accounting processes should evolve in an AI world - rather than being limited to what they are today. You’re a good fit for this role if you’ve thought deeply about how your current work might look different in just a few years time and want to help usher in this transformation. This requires a clear conception of the fundamentals of accounting, an ability to quickly understand disparate workflows, and the vision to redefine the current process. 📍 Location: NYC, Flatiron office. In-person team. What you’ll be doing You’ll work closely with our engineering and design teams to build out new features and capabilities, rigorously think through edge cases, and prioritize diligently to ship what matters. You’ll listen to users and translate their needs into workflows that help them do their best work with AI—while also drawing on your own knowledge and experience to anticipate
