Senior Technical Product Manager (Document Intelligence)

Datasnipper; Netherlands; On-site; EUR 110,000 - 150,000; Full time

Job summary

DataSnipper is seeking a Technical Product Manager to own the document intelligence platform, shaping the ingestion, parsing, and extraction of unstructured documents into structured, queryable data. You’ll drive architecture and quality bars, collaborating with ML and backend teams to deliver scalable, reliable data foundations for agentic workflows.

You’ll translate customer and internal needs into APIs, workflows, and internal platform roadmaps, balancing accuracy, throughput, and cost.

Qualifications

• 4+ years in product management, with 2+ years on technical/platform/data products (APIs, infrastructure, data pipelines, ML systems, or developer tools).
• Hands-on evals or quality-measurement methods for AI/ML or data systems at scale.

Responsibilities

• Own the document intelligence pipeline and the tooling that ingests, parses, extracts, classifies, and summarizes unstructured documents.
• Define product strategy for the document intelligence platform and translate needs into requirements and a tooling roadmap.
• Oversee storage, indexing and retrieval of processed documents and derived data for fast, reliable agentic workflows.
• Own non-functional requirements: accuracy, throughput, latency, cost per document, reliability, observability.
• Lead evals to measure extraction, classification, and summarization quality at scale; collaborate with LLM Ops on eval infra.
• Coordinate with ML and backend engineers, drive architecture trade-offs (cost vs accuracy, coverage vs latency).
• Partner with Integrations to manage data ingress and results egress for agent teams.

Skills

Product management

Technical PM

Data pipelines

ML systems

System architecture

Tools

APIs

ML tooling

Job description

• 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
• 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

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 youA 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 itselfHighly 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 ambiguityA tinkerer. You build to understand — you’ll prototype an extraction or test a classification approach rather than theorize about itA systems-and-data mind. You think in pipelines, data quality, and infrastructure that holds up under volume and varietyBackground in distributed systems, data infrastructure, or pipelines — you understand how processing systems behave under scale, volume, and variety4+ 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 scaleCan 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-offsTreats non-functional requirements as a first-class product surface: accuracy, throughput, latency, cost, reliability, observabilityStrong 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 toolFormer software engineer, ML engineer, or data scientist who moved into productHands-on experience with document AI / IDP, OCR, NLP, or unstructured-data systems specificallyA public point of view on AI or document intelligence — writing, talks, OSS — or the appetite to build oneExperience in audit, accounting, or financial services (domain context for the documents we process)