ClickHouse

Technology

AIProductEngineer

$165–235k ~AI est. United States Remote Friendly
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid+ candidates.

The Brief

“AI Product Engineer at ClickHouse. Skills: AI Product Engineering, Agent development, Observability, LLM systems. Build agents that investigate incidents. Surface anomalies”

Industry & Context.

Technology
Problems you'll solve

Root cause analysis; Troubleshooting; Debugging; Latency; Cost; Context window limits; Hallucinations

What They're Looking For.

Must Have

5+ years software engineering experience, 1-2 years LLM-powered systems experience, Backend skills in TypeScript/Node.js, Backend skills in Python, Hands-on experience building agents, Experience designing skills, Experience with MCP, SQL proficiency, Comfort with Docker, Comfort with Kubernetes

Nice to Have

Production agents in observability experience, Production agents in incident response experience, Production agents in SRE experience, Opinions on agent observability, Experience with prompt caching, Experience with context compaction, Experience with columnar databases, Experience with event ingestion pipelines, Contributed to open source AI/agent project, Maintained open source AI/agent project, Familiarity with Go, Familiarity with Rust

What You'll Do.

Build agents that investigate incidents

Answer 'why is production broken?'

Use ClickStack as substrate

Build reusable skills library

Capture team debugging methods

Capture team root cause analysis

Capture team ClickHouse query writing

Capture team incident response

Own agent stack end-to-end

Manage context engineering

Ensure agent works in production

Make ClickStack run AI workloads

Allow agents to read telemetry

Allow agents to take action

Allow agents to stay observable

Collaborate with OSS contributors

Collaborate with customers

Debug customer problems

Feed learnings into product

Tackle latency issues

Tackle context window limits

Tackle eval coverage issues

Tackle hallucinations on telemetry

How You'll Work.

Team & Collaboration

OSS contributors; Customers; Developer community

Full Job Description

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 engineer

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