ClickHouse

AI Product Engineer - ClickStack

Canada (remote)

Sponsorship not specifiedDetected 46 days ago

TypeScriptGoRustNode.jsSQLDockerLLMsIncident Response

About the role

About ClickHouse Recognized on the 2025 Forbes Cloud 100 list, ClickHouse is one of the most innovative and fast-growing private cloud companies.
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.

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.
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.
Build agents that investigate incidents.

5+ years of software engineering experience, including 1-2 years on LLM-powered systems or agents in production.
Experience designing skills (Markdown-based workflow encodings, Anthropic-style or similar) and a clear view on when a skill, a tool, or both is the right fit.
Experience with MCP: building servers, designing tools, and thinking through auth, scoping, and observability for agentic systems.
SQL proficiency - you can write ClickHouse queries directly.
Comfort with Docker and Kubernetes.
Experience with prompt caching, context compaction, or other techniques relevant to running agents on production telemetry volumes.
Experience with columnar databases and event ingestion pipelines.
Familiarity with Go, Rust, or other systems languages for integrations and high-throughput infra.
Strong backend skills in TypeScript/Node.js and/or Python. Comfortable in both, even if one is primary.
Hands-on experience building agents: multi-step tool use, planning, memory, error recovery. You've shipped them and dealt with the failure modes.
Strong evals practice: golden sets, LLM-as-judge, regression detection.
Active in open source and the developer community.
Built or operated production agents in observability, incident response, or SRE.
Strong opinions on agent observability - tracing, cost attribution, eval pipelines, OpenTelemetry for agents - and ideas on how to improve it.
Contributed to or maintained an open source AI/agent project.

About ClickHouse
Recognized on the 2025 Forbes Cloud 100 list, ClickHouse is one of the most innovative and fast-growing private cloud companies.
The company's sustained, accelerating momentum was recently validated by a $400M Series D financing round.

For roles based in the United States, t he typical starting salary range for this position is listed above.

Healthcare - Employer contributions towards your healthcare.
Equity in the company - Every new team member who joins our company receives stock options.
Time off - Flexible time off in the US, generous entitlement in other countries.
Flexible work environment - ClickHouse is a globally distributed company and remote-friendly.
A $500 Home office setup if you're a remote employee.

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.
As part of a rapidly scaling start up, you will be instrumental in shaping our culture.
Are you interested in finding out more about our culture?
Learn more about our values here.

This listing is sourced directly from ClickHouse's careers page and normalized into a canonical job model.