AI Product Engineer - ClickStack

About ClickHouse

Recognized on the 2025 Forbes Cloud 100 list, ClickHouse is one of the most innovative and fast-growing private cloud companies. With more than 3,000 customers and ARR that has grown over 250 percent year over year, ClickHouse leads the market in real-time analytics, data warehousing, observability, and AI workloads.

The company’s sustained, accelerating momentum was recently validated by a $400M Series D financing round. Over the past three months, customers including Capital One, Lovable, Decagon, Polymarket, and Airwallex have adopted the platform or expanded existing deployments. These customers join an established base of AI innovators and global brands such as Meta, Cursor, Sony, and Tesla.

We’re on a mission to transform how companies use data. Come be a part of our journey!

Join us in building the AI layer for Observability!

ClickStack is the open-source observability platform we're building at ClickHouse — logs, metrics, traces, and session replays unified so engineers can find root causes quickly. The interesting work now is in the agent layer: systems that can investigate an incident at 2 AM, propose a root cause, and hand the on-call a concise summary by the time they've logged in.

We're hiring an AI Product Engineer to build agentic capabilities on top of a petabyte-scale observability platform, with a focus on developer experience. If you've been building agents, designing skills, and wiring up MCP servers — and you've hit the limits of generic copilots for production work — we'd like to talk.

What you'll do

• Build agents that investigate incidents. They surface anomalies, answer "why is production broken?", and use ClickStack as their substrate.
• Write skills, not just prompts. Build a library of reusable skills that captures how our team debugs, finds root causes, writes ClickHouse queries, and runs incident response, so agents pick up the right playbook instead of starting from scratch.
• Own the agent stack end-to-end. Context engineering, tool design, evals, tracing, cost. You're responsible for whether the agent works in production.
• Make ClickStack a great place to run AI workloads. Build the MCP servers, SDKs, and integrations that let customers' agents read telemetry, take action, and stay observable themselves.
• Work in the open. Collaborate with OSS contributors and customers, debug their problems alongside them, and feed what you learn back into the product.
• Tackle the hard parts. Latency, cost, context window limits, eval coverage, hallucinations on real telemetry.

Who you are

• You've been building agents long enough to have opinions — about context engineering, tool design, when to use a skill vs. a tool, what evals catch and miss, and where popular frameworks break down.
• You think in production terms: p99 latency, cost per task, whether the system still works next we