We're looking for a Technical PM to own the document intelligence platform — the tooling that turns messy, unstructured documents into clean, structured, agent-ready data. Extraction, classification, summarization, storage, indexing, and retrieval of document-based data are the raw material every agentic workflow at DataSnipper depends on. This is a technical, engineering-facing role focused on tool and data infrastructure, not agent orchestration. You’ll own the critical extraction and processing layers that drive the core of our agentic work. If the extraction is wrong, every agent downstream inherits the error; your job is to make the data foundation accurate, fast, and reliable at scale. It's a product role at heart: you translate what customers and the agent teams need into the architecture and quality bar that delivers it — product judgment expressed through technical decisions. You'll spend your time in pipeline and data-architecture discussions alongside ML and backend engineers, defining extraction quality and the evals that measure it. When you work with go-to-market, it's to turn accuracy, coverage, and reliability into a story the market trusts. If you're passionate about turning messy, unstructured documents into clean, reliable data — and you've dug deep on something like extraction, classification, parsing/OCR, or measuring data quality at scale — this role is for you. About 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 • The document intelligence pipeline. Technical direction for how we ingest, parse, extract, classify, and summarize unstructured documents — the tooling that converts raw documents into structured, queryable data. Accuracy and coverage across document types are your core mandate. • Product strategy. Own the product strategy for the document intelligence platform, translating customer and agent-team needs into clear requirements and the tooling roadmap (APIs, workflows, and internal platforms) that makes those needs shippable and maintainable. • Document storage, indexing & retrieval. How processed documents and their derived data are stored, indexed, and served — so agentic flows can retrieve the right information quickly and reliably. • Non-functional requirements of the platform. How well the platform runs — extraction accuracy, throughput, latency, cost per document, reliability, scalability across document volume and variety, and observability. These are your primary success metrics, not feature counts. • Quality & evals. Own the creation and running of the evals that measure and improve extraction, classification, and summarization quality at scale — defining the quality bar each capability meets before it ships. You work hand in hand with our LLM Ops team, who own the eval infrastructure; you own the evals themselves and what they tell us. • Model & tooling strategy. Decisions on models, extraction techniques, and build-vs-buy across the document-processing stack; cost/performance/accuracy trade-offs; staying current as document-AI techniques evolve. • Cross-functional partnership. Engineering is your primary partner — you operate as a technical peer to engineering managers and tech leads. You serve the agent teams (your data is their foundation) and partner closely with the Integrations team on how documents get in and results get out. Who you are • A tinkerer. You build to understand — you'll prototype an extraction or test a classification approach rather than theorize about it. • A product thinker in an engineer's seat. You don't need to be an auditor, but you love representing the customer and the business problem — and turning that into a robust, well-architected data platform. The technical depth is in service of product value, not an end in itself. • A systems-and-data mind. You think in pipelines, data quality, and infrastructure that holds up under volume and variety. • Highly autonomous and entrepreneurial. You find the problems that matter, set direction, and drive without waiting to be told. You treat your area like your own company — scrappy, outcome-obsessed, comfortable under ambiguity. What you'll bring Must-have: • 4+ years in