Senior Technical Product Manager (Document Intelligence)

Join DataSnipper as a Senior Technical Product Manager for our document intelligence platform. In this role, you will own the technical direction for ingesting, parsing, extracting, classifying, and summarizing unstructured documents. You will also be responsible for product strategy, document storage, indexing and retrieval, non-functional requirements of the platform, quality and evaluations, model and tooling strategy, and cross-functional partnership. This is a highly autonomous and entrepreneurial role that requires a strong background in product management, technical/platform/data products, and document or data processing.

• Ownership of the document intelligence pipeline, including technical direction for ingestion, parsing, extraction, classification, and summarization of unstructured documents.
• Responsibility for product strategy, translating customer and agent-team needs into clear requirements and the tooling roadmap that makes those needs shippable and maintainable.
• Creation and running of evaluations that measure and improve extraction, classification, and summarization quality at scale, defining the quality bar each capability meets before it ships.
• 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
• 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
• 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
• A tinkerer. You build to understand — you'll prototype an extraction or test a classification approach rather than theorize about it
• A systems-and-data mind. You think in pipelines, data quality, and infrastructure that holds up under volume and variety
• Background in distributed systems, data infrastructure, or pipelines — you understand how processing systems behave under scale, volume, and variety
• 4+ years in product management, with 2+ years on technical/platform/data products (APIs, infrastructure, data pipelines, ML systems, or developer tools)
• Hands-on experience creating and running evals or quality-measurement methods for AI/ML or data systems at scale
• 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)
• Fluency in model and tooling operations: model/technique selection, build-vs-buy, cost/performance/accuracy trade-offs
• Treats non-functional requirements as a first-class product surface: accuracy, throughput, latency, cost, reliability, observability
• 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
• 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)