Senior Technical Product Manager — Agent Runtime

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.

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 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)

• 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)