Research Engineer, AI/ML

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.

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.