Senior Product Manager, Agent Builder

DataSnipper

Remote · New York$195K – $220KDiposting 5 hari yang lalu Series B

Tentang peran ini

We are looking for a skilled and passionate Senior Product Manager to join our Agent Builder team in our NYC office. Working in a high-growth scale-up, you’ll have the unique opportunity to drive innovation at scale while contributing to a company transforming the world of audit and finance.

ABOUT THE ROLE:

Agent Builder is where firms turn their own methodology and knowledge into AI agents, and manage those agents for their whole life, from definition to retirement. A generic agent knows nothing about how a firm works. Its methodology, guidance, templates and past work are what make an agent behave like one of the firm's own people, and that knowledge and context layer is what sets our agents apart.

You'll own the Agent Builder product in Alwin, our agentic platform for audit and finance. That means the definition model behind an agent, how firms test and publish agents they can trust, and how those agents are versioned, governed and retired at scale. There's no playbook for letting professionals build, run and govern production agents in a regulated domain. You'll help write it.

This is a product role with a strong technical bent, and we welcome candidates from product or technical PM backgrounds. You'll work closely with a new engineering team in New York, with our Agent Runtime, Evals and Document Intelligence teams in Amsterdam, and with the customers and domain experts who define what a good agent looks like.

WHY DATASNIPPER?

The hardest AI problems aren't about getting an answer fast. They're about getting one people can trust.

We believe trust is essential to how society works. Every day, we rely on information that's been independently verified, from financial statements to the products we buy, to the sustainability claims companies make. Behind that trust are audit and finance professionals who examine evidence, verify information and hold organizations accountable.

At DataSnipper, we build AI solutions that empower those professionals. By making evidence easier to trace and verify, we help them focus on the judgment and expertise that make their work so important.

For our product and engineering team, this is where things get exciting. In audit, a plausible answer isn't good enough. Every output has to be explainable and traceable back to its source. We've just launched a brand new agentic platform, Alwin by DataSnipper. AI agents running in the cloud across hundreds of documents at once, reasoning across PDF, Word and Excel, with a full audit trail behind every step. There are lots of greenfield problems ahead, and real room to shape what we build and how we build it.

And we build at scale. More than 600,000 professionals across 3,000+ organizations in 175 countries rely on DataSnipper, including Fortune 500 companies, government agencies and all Big Four audit firms. In 2025 alone, the DataSnipper platform delivered more than $1.4 billion in productivity savings to is customers. Backed by a $100 million Series B led by Index Ventures, at a $1 billion valuation, and as proud members of 1% for the Planet, we care about how we grow, not just how fast.

If you want to build AI that professionals trust with work that really matters, this is your chance to help shape the future of audit and finance and do the best work of your career along the way.

WHAT YOU'LL OWN

- Knowledge and context: how a firm's methodology, guidance, templates and prior work are captured, kept current, permissioned and served to agents at the moment they need them

- Definition: the authoring experience and the definition model that turn a procedure into a multi-step agent: steps, tools, knowledge sources, thresholds and human-in-the-loop gates

- Test and publish: how builders evaluate an agent against real cases before it ships, and the review and approval it passes before it reaches the Agent Library

- Run and observe: tracing what an agent did on a live engagement, which knowledge it used and why

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