Senior Technical Product Manager — Document Intelligence

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.

Audit and finance are still massively manual and we are changing that. DataSnipper is a $1B, bootstrapped unicorn with 600,000+ users across 180+ countries, already embedded in the daily workflows of top audit and accounting firms.

Now, we are taking things further with our Excel Agent, bringing AI directly into where the work actually happens. Unlike generic AI tools, we do not sit on the sidelines. Our AI operates inside Excel, with access to real documents and audit evidence, meaning it does not just generate answers, it does the work, with full traceability.

We are not just applying AI, we are redefining how audit gets done. If you want to build something category-defining at scale, this is the place.

• 4+ years in product management, with 2+ years on technical/platform/data products (APIs, infrastructure, data pipelines, ML systems, or developer tools)

• Strong grounding in document or data processing: extraction, classification, parsing/OCR, summarization, or other intelligent-document-processing / NLP techniques — with enough grasp of LLM-based approaches to judge when they're the right tool

• Background in distributed systems, data infrastructure, or pipelines — you understand how processing systems behave under scale, volume, and variety

• Treats non-functional requirements as a first-class product surface: accuracy, throughput, latency, cost, reliability, observability

• Hands-on experience creating and running evals or quality-measurement methods for AI/ML or data systems at scale

• Fluency in model and tooling operations: model/technique selection, build-vs-buy, cost/performance/accuracy trade-offs

• Can write technical specs that engineers review for feasibility (not correctness) and prototype with code to validate hypotheses; comfortable with architecture trade-offs (accuracy vs. cost, coverage vs. latency)

• Former software engineer, ML engineer, or data scientist who moved into product

• Hands-on experience with document AI / IDP, OCR, NLP, or unstructured-data systems specifically

• A public point of view on AI or document intelligence — writing, talks, OSS — or the appetite to build one

• Experience in audit, accounting, or financial services (domain context for the documents we process)