Senior Software Engineer, Agent Tracing
Sentry
- Pay
- EUR 81k–220k
- Workplace
- Hybrid
- Location
- Vienna, Austria · Vienna · Austria
- First seen
- 1 hour ago
- Last seen
- 1 hour ago
- Board
- Ashby
Summary
ABOUT SENTRY Software runs the world and the pace is faster than ever.
- python
- typescript
- react
- postgres
- django
Posting
ABOUT SENTRY Software runs the world and the pace is faster than ever. Sentry helps developers fix errors and performance issues before users notice, so teams can spend less time firefighting and more time building. Trusted by 200,000+ organizations, Sentry is today’s application monitoring standard and our team is building its AI-native future. ABOUT THE ROLE Agents are becoming the default way developers ship LLM-powered software, and they are miserable to debug. A single run fans out across model calls, tool calls, retries, handoffs between agents, and multi-turn conversations that only make sense as a whole. When one goes wrong, the developer usually has a log line and a shrug. Sentry's Agent Tracing https://docs.sentry.io/product/agents/ turns that into something you can actually inspect: agent runs, tool calls, model interactions, token usage and cost, conversations you can replay message by message. As a Senior Software Engineer on the Telemetry Experience team, you'll own that product surface - Python/Django and Postgres on the backend, React and TypeScript on the frontend, sitting on top of Sentry's trace storage. You'll work with the SDK engineers who instrument the agent frameworks upstream and own everything customers touch downstream: the dashboards, the conversation views, the APIs, and the workflows that turn a confusing agent run into an obvious fix. IN THIS ROLE YOU WILL - Own projects end to end. Take a problem from a rough customer complaint through scoping, design, implementation across the stack, rollout, and measurement. You'll write the design doc, cut the work into shippable pieces, and stay with it until the numbers move - Build the views developers debug agents in. Multi-agent workflows, conversation replay, cost and token breakdowns, the moment where a run failed and why. Making a deeply nested, wildly variable trace legible is a genuinely hard interface problem - Make the data underneath fast and queryable. Filtering and sorting millions of conversations, getting cost calculation right, and designing APIs that both humans and other people's agents can use well - Detect the failures worth flagging. We're expanding from showing developers what happened to telling them what went wrong - common agent failure classes surfaced as issues, not as something you have to go looking for - Take the friction out of getting started. Setup should be trivial on any stack, whether a developer wires it up by hand or asks the