Automate your B2B revenue process with Tabs - the AI-powered platform for contract-to-cash automation. Streamline invoicing, payments, reporting, and more. Get a demo now! Backed by General Catalyst and Lightspeed.
About the role
You’ll work on a fast-moving AI team, owning problems from initial exploration through production. We’re a small team, so everyone has a hand in deciding what to build and making it work in the real world. Found on 1752vc Careers, the job board for startup and VC roles.
What they're looking for
- 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
More about this role
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...
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