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
The Work
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:
Product Engineering
Agent Engineering (e.g. context engineering, tool design)
Agent Platform (e.g. harness engineering, eval systems)
Platform & Infrastructure
Agent Data (e.g how we ensure accurate & low latency external data for agents)
Atlas — the mandate to build the AI systems powering Basis from recruiting to sales to engineering. The Atlas team builds the context layer, internal agents, and knowledge systems that will eventually produce the majority of total output at Basis.
Some problems we think about
How do we ensure users feel as comfortable working with nondeterministic systems (agents) as they do with deterministic software?
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."
How do we manage agent state over long horizons?
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?
At what level of abstraction do we design tools for agents?
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.
How do we extract dense signal from long-running agent trajectories?
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 can we engineer our monorepo to be more ergonomic for agents?
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?
Our beliefs about the future of technical work:
We believe that as agents become more capable, two themes are emerging:
The builder stack collapses
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.
Engineering > Coding Anything that can be clearly defined and verified will be delegated to an agent. What remains is deciding what to build, breaking down problems from first principles, and designing systems that compound.
The kind of engineer who does well here
We hire individuals, not resumes. Given how fast things are changing, it's more important to hire the best people than to hire the person with specific experience.
First principles and systems thinking. Many of the problems we solve are net new. There's no playbook. We want people who can decompose a novel problem, reason from fundamentals rather than pattern match, and design things that compound.
Thinking beyond the engineering. The engineers who do best here understand the domain deeply: the data systems, the workflows, the work our agents are performing. That understanding is what lets you design better systems, train better agents, and make better product decisions.
Ownership of outcomes. Every project at Basis has a single Responsible Party (RP) who is accountable for whether it ships and whether it works. There are always more things to own than people to own them. Engineers own their own work at Basis. Read more about our RP system here
Agency. Having the initiative to pick up the context you need to make good decisions. Talking to the right people, reading the data, understanding the problem deeply enough to know what matters.
Non-negotiables:
In person in Flatiron, NYC 5 days a week
Excited about working with coding agents (we give every engineer an unlimited token budget)
Excited about working at the frontier of applying agents to real economy problems
Benefits at Basis
We offer a competitive and thoughtful benefits package designed to support your physical, mental, and financial well-being:
Health & Wellness: Premium Medical, Dental, and Vision coverage; Life Insurance; and 6 coaching & 6 therapy sessions through Spring Health.
Time off: Unlimited PTO + 12 paid company holidays.
In-Office Perks: Daily meal stipends, a fully stocked kitchen, and $300 toward your custom desk setup.
Financial Benefits: Pre-tax commuter benefits and 401(k) retirement plan
Team Culture: Monthly office activities and frequent optional team happy hours.
Parental Leave