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Salary: USD 125,000 - 150,000 / annual
Sentry is an application monitoring platform trusted by 200,000+ organizations. The company is building the AI-native future of error and performance monitoring for developers.
In this role, you will redefine technical support by combining deep human expertise with autonomous agentic systems. You are a debugger of both code and systems, treating support volume as a data signal to build automated resolution paths. You will engage with elite developers across GitHub, Discord, and internal systems while acting as the Technical Lead for Agentic Operations.
Key Responsibilities:
- Perform root-cause analysis on complex issues and distributed tracing gaps across polyglot environments, serving as a strategic consultant for senior engineers at enterprise customers.
- Troubleshoot SDK implementations by diving into source code to help developers instrument complex frameworks and custom environments.
- Act as the bridge between developers and product teams, using support data to prioritize high-impact platform fixes.
- Engineer deterministic support paths and autonomous resolution logic for support agents, moving beyond probabilistic guesses to high-fidelity outcomes.
- Build knowledge engineering systems and agentic tooling using MCP (Model Context Protocol) and Sentry's internal APIs to create "skills" for autonomous agents.
- Apply the "Seer Philosophy" (proven AI-driven production fixes) to the support lifecycle, transforming from a Help Desk to an Autonomous Resolution Engine.
You will thrive in this role if you love eliminating non-scalable effort, enjoy full-stack troubleshooting across frontend/backend/infrastructure, prefer evidence-based decisions over anecdotes, and want to guide the industry on how elite engineers and AI co-exist.
Requirements:
- 5–8+ years of experience in technical support engineering, systems engineering, or software development, with a track record of solving high-complexity SaaS problems.
- Strong coding foundations: mastery of Python, JavaScript, Java, or Ruby; comfort reading SDK source code and identifying bugs in production-level repositories.
- Deep understanding of distributed systems, microservices, APIs, and the full path from frontend to database.
- Practical familiarity with LLM orchestration (RAG, context windows, system prompts) and critical judgment about where AI succeeds versus where human intervention is required.
- Exceptional communication skills: ability to explain complex architectural concepts to humans and write precise system instructions for AI.
Bonus Qualifications:
- Active open-source contributor with GitHub presence or experience maintaining open-source libraries.
- Former full-time software developer with direct experience of what keeps customers up at night.
