Senior Technical Product Manager — Agent Runtime
DataSnipper · Amsterdam
We're looking for a Technical PM to own the agent runtime and the platform that powers our agentic products — and the non-functional requirements that make those agents accurate, fast, reliable, and cost-effective at scale.
You'll own how well the platform runs, not the end-user features on top of it. You'll define the runtime architecture, own the product evals that prove our agents stay accurate as we scale and upgrade models, and set the bar for latency, reliability, cost, and observability across our agent runtimes.
This is an engineering-facing role — but it's a product role at heart. You translate what customers and the business actually need into the architecture that delivers it: product judgment expressed through technical decisions. You'll spend your time in architecture discussions and eval design alongside ML and backend engineers, not in design reviews. When you work with go-to-market, it's to turn runtime scale, stability, and reliability into a story the market trusts. And you'll track how the frontier of agentic engineering is moving — keeping us ahead of it and being a visible voice on it inside and outside the company.
If you've never written a prompt chain, debugged a pipeline, reasoned about token cost, or designed an eval, this isn't the right fit.
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
Agent runtime architecture. Technical direction for our agent runtimes: agent orchestration and multi-step loops, context and memory management, retrieval strategy, tool execution and orchestration, model routing and fallbacks, guardrails, caching, streaming, and concurrency.
Non-functional requirements of the platform. How well the platform runs - latency, throughput, reliability, graceful degradation, scalability under concurrent agent load, token-cost economics, security, and observability/telemetry. These are your primary success metrics, not feature counts.
Product evals. Own the creation and running of the evals that prove our agents are accurate and reliable - defining the quality bar every agent meets before it ships, and the measures that let us improve and upgrade models with confidence. 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 strategy. Model selection, swaps, and provider decisions; cost/performance trade-offs; staying current as frontier models move.
Market intelligence & thought leadership. Track how the field is building agentic systems. Own a point of view on what differentiates us and where the runtime must go to stay ahead - and evangelize it both internally and externally (talks, writing, customer and industry conversations).
Cross-functional partnership. Engineering is your primary partner - you operate as a technical peer to engineering managers and tech leads, not just a prioritization partner. You translate experience-team needs into runtime capability, and partner with go-to-market on scale, stability, and reliability.
Who you are
A tinkerer. You build to understand - prototypes, prompt chains, quick experiments. You'd rather try it 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 understanding into strong architecture. The technical depth is in service of product value, not an end in itself.
Highly autonomous. You operate with little direction: you find the problems that matter, set the direction, and drive them without waiting to be told.
Entrepreneurial. You treat your area like your own company - scrappy, outcome-obsessed, and comfortable making the call under ambiguity.
What you'll bring
Must-have:
4+ years in product management, with 2+ years on technical/platform products (APIs, infrastructure, ML systems, or developer tools)
Deep understanding of LLM-based systems and agent architectures: prompting, tool use, planning, multi-step agent loops, context and memory, multi-agent systems - with enough grasp of retrieval/vector search to judge when it's the right tool
Background in distributed systems or infrastructure - you understand how runtimes behave under scale and how a change ripples across the stack
Treats non-functional requirements as a first-class product surface: latency, reliability, scalability, cost, observability
Hands-on experience creating and running evals that measure and improve agent accuracy and reliability at scale
Fluency in model operations: model selection and swaps, provider trade-offs, token cost/performance optimization
Can write technical specs that engineers review for feasibility (not correctness) and prototype with code to validate hypotheses; comfortable with architecture trade-offs (latency vs. accuracy, cost vs. capability)
Strong-to-have:
Former software engineer, ML engineer, or data scientist who moved into product
A public point of view on AI/agents - writing, talks, OSS - or the appetite to build one
Experience in audit, accounting, or financial services (domain context for what the agents do)
What We Offer
Be part of one of the fastest-growing, profitable unicorn scale-ups in the Netherlands with a global impact.
Equity (Stock Appreciation Rights) to share in the company’s success and growth.
Pension plan with a 6% contribution on top of your base salary.
28 vacation days per year (full-time) to support your work-life balance.
Hybrid work model with at least 3 days onsite in our dynamic Amsterdam office.
Daily, freshly prepared lunches by our in-house chef to keep you energised.
NS business card for easy commuting to the office.
A structured onboarding programme designed to set you up for success, including dedicated time to learn our product and customers before you hit the ground running.
Access to continuous learning and development initiatives to grow your skills.
Engage with a vibrant international team spread across seven global offices.
Company-wide events like DataSnipper GO, where global teams come together.
Access to OpenUp, a mental health and wellness platform supporting your wellbeing.
Recruitment steps
Recruiter screen
Hiring Manager interview
Assessment interview
Final interview with Engineering leadership
At DataSnipper, we believe great ideas come from different perspectives, experiences, and backgrounds. We are committed to building an inclusive workplace and providing every candidate with a fair and equitable hiring experience.
Our hiring decisions are merit-based and grounded in the skills, experience, potential, and capabilities relevant to the role.
We welcome applicants regardless of age, race, ethnicity, nationality, gender, gender identity or expression, sexual orientation, religion, disability, or any other characteristic that makes you, you.
What matters to us is what you can bring to the role, the team, and DataSnipper. 🚀