Member of Technical Staff (All Levels)

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

Being a Member of Technical Staff at Basis means you'll face changing problems. You might spend a week designing the context ontology for an agent, then shift to building the data pipelines to hydrate agent state.

We don't have static functional teams. We have pods that exist around areas and objectives which reform every quarter as our goals update and the team expands. It's common to see engineers do core infra work one quarter and product agent work the next. It's also common for engineers to deeply specialize in certain technical areas.

As a Member of Technical Staff, you can do work across:

• We say around here that "users are already used to working with non-deterministic systems, it's just those systems are their coworkers, not their computers."

• What should ETL workflows look like when agents operate for hours? How should state hydration work? How much agency do we grant the agent to decide how its own data state is managed?

• Too low and the agent wastes effort on mechanical steps. Too high and it can't handle edge cases. The right answer changes as models get smarter.

• An agent runs for five hours across thousands of decisions. How do we attribute outcomes back to specific reasoning steps? How do we tune eval judges when the judgement includes subjectivity?

• How do agents verify their own work? How can we ensure code reviews by people focus on the important technical questions, rather than correcting slop?

We believe that as agents become more capable, two themes are emerging:

• Coding agents provide the ability to move across domains in ways that weren't possible before

• You can offload more decision making to the agents inside your product meaning the lines between ML work and eng work start to disappear.

• Approx 20% of product engineering at Basis is teaching agents to tackle non-deterministic workflows. We see that being 70% by end of year.

The distinctions between frontend vs. backend, infra vs. product, ml vs. eng are already melting away.