Tabs is the AI Operating System for Revenue, built for modern finance and accounting teams. It combines deep revenue and accounting expertise with the agents and applications needed to run revenue work end to end. Tabs understands customer and contract context, applies accounting logic, and executes critical workflows with built in controls, auditability, and human oversight. With Tabs, finance teams can move from manually managing revenue workflows to directing outcomes while the system executes the work.
The Job
You’ll work on a fast-moving AI team, owning problems from initial exploration through production.
Turn messy financial data and ambiguous problems into working AI products, starting with simple baselines and adding complexity only when it earns its keep
Build evaluations that reflect real user outcomes, then use error analysis, ablations, and production feedback to make the system better
Make practical tradeoffs across model quality, cost, latency, determinism, reliability, and maintainability
Partner closely with product and engineering to build AI features that take real work off finance teams’ plates
Strong statistical and machine learning fundamentals, with good judgment about when the answer is classical ML, an LLM, agents, or something in between
Experience shipping ML or AI systems end-to-end, from data and evaluation through production
Comfort making progress with noisy data, weak labels, incomplete specifications, and imperfect supervision
Experience across several of: classical ML, embeddings, retrieval and reranking, similarity search, model evaluation, LLM applications, and agentic systems
Strong Python skills and the ability to contribute to production software; TypeScript or modern web application experience is a plus
We welcome a range of backgrounds. Successful candidates will typically have one of the following:
A bachelor’s degree in a relevant quantitative field plus 3+ years of relevant industry or applied research experience
A relevant master’s degree plus 1+ year of relevant industry or applied research experience
A relevant PhD; doctoral research counts as relevant experience, with 3 years of substantive doctoral research considered equivalent to the experience above
Equivalent practical experience demonstrated through shipped systems, independent research, open-source work, or another nontraditional path
We’re a small team, so everyone has a hand in deciding what to build and making it work in the real world.
We ship, learn from real usage, and iterate
We make assumptions explicit, follow the evidence, and communicate tradeoffs clearly
We stick with hard problems and welcome better ideas, regardless of where they come from
We help where the team needs us, even when it falls outside our immediate scope
We collaborate closely in person five days a week
No one gets extra points for making the solution more complicated than the problem.
Even if you don’t meet every qualification, we encourage you to apply. We care most about curiosity, craft, judgment, and drive.
Perks and Benefits (Full-time Employees)Competitive compensation and equity
Unlimited PTO
Up to 100% employer covered monthly healthcare premium (medical, dental, vision)
Lunch provided via Sharebite, plus dinner for any later in office days.
Parental leave up to 12 weeks
Tax free commuter and parking benefits
Voluntary insurances (Life, Hospital, Critical Illness, Accident)
Employee Assistance Program (Rightway)
Free One Medical Membership
401k
Tabs is an equal opportunity employer. We welcome teammates of all identities and do not discriminate on the basis of race, ethnicity, religion, gender identity, sexual orientation, age, disability, veteran status, or any other protected characteristic. We’re committed to creating an environment where everyone can grow, contribute, and feel comfortable being themselves.
Skills Required
- Strong statistical and machine learning fundamentals
- Experience shipping ML or AI systems end-to-end, from data and evaluation through production
- Experience working with noisy data, weak labels, incomplete specifications, and imperfect supervision
- Experience with several of classical machine learning, embeddings, retrieval and reranking, similarity search, model evaluation, LLM applications, or agentic systems
- Strong Python skills
- Ability to contribute to production software
- Bachelor's degree in a relevant quantitative field plus 3 or more years of relevant industry or applied research experience
- Relevant master's degree plus 1 or more year of relevant industry or applied research experience
- Relevant PhD, with doctoral research counted as relevant experience
- Equivalent practical experience demonstrated through shipped systems, independent research, or open-source work
- TypeScript or modern web application experience
What We Do
Finance software transformed how companies manage spend years ago. Revenue never got the same treatment. Billing, collections, and revenue recognition stayed manual, because the accounting complexity and the risk of getting it wrong made the work hard to automate. So finance teams scaled with headcount instead. As revenue grew, so did the exceptions, judgment calls, and spreadsheet work needed to support it. Meanwhile, revenue itself got more complicated. Companies now price with usage, credits, commitments, consumption, outcomes, and hybrids of them. Products change, pricing changes, and sometimes the unit of value itself changes. Static systems were never designed for that variability. We started Tabs in 2023 to fix this with AI. Today we run the contract-to-cash lifecycle as one connected system: contracts, billing, collections, payments, revenue recognition, and reporting.
Why Work With Us
Founded by operators who’ve spent decades scaling companies and backed by Lightspeed, General Catalyst, and Primary, you will have the opportunity to join a team that is rethinking how revenue runs from contract to cash.
