D
Datasnipper
Senior Technical Product Manager (Agent Runtime)
Skills
Product ManagementMachine LearningAIProduct Manager
2 months ago
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Important information
Contract type:
Permanent
Location:
Netherlands
Work mode:
Onsite
Published on:
10 September 2026
What they need
- 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
- 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- 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
- 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
- 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
- Background in distributed systems or infrastructure - you understand how runtimes behave under scale and how a change ripples across the stack
- 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
- 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)
- 4+ years in product management, with 2+ years on technical/platform products (APIs, infrastructure, ML systems, or developer tools)
- Fluency in model operations: model selection and swaps, provider trade-offs, token cost/performance optimization
- Experience in audit, accounting, or financial services (domain context for what the agents do)
- A public point of view on AI/agents - writing, talks, OSS - or the appetite to build one
- Former software engineer, ML engineer, or data scientist who moved into product
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