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At Sentry, support is an engineering discipline. Our customers are developers building the future of software, and they deserve answers that go deeper than a knowledge base link. We are redefining the standard of technical support by combining deep human expertise with advanced AI agents. We need you to not just solve complex technical issues, but to architect the systems that help us scale that expertise to millions of developers. Sentry Support Engineers don't just clear queues; they are orchestrators. You will engage with our users across GitHub, Discord, and our support systems, while also acting as the product owner for our AI Agents. You ensure that when a developer asks a complex question, our AI has the right context, the right data, and a seamless "Human-in-the-Loop" path to you when deep expertise is required.
Job Responsibility:
Master the Sentry Ecosystem: Become a Platform Expert: Develop deep authority on Sentry’s features to guide customers through complex debugging workflows
Resolve Technical Inquiries: Answer inbound support requests with a focus on teaching and enabling developers, not just closing tickets
Troubleshoot SDK Implementations: Go beyond the UI to help developers instrument Sentry’s SDKs within their specific codebases and frameworks
Manage Bugs & Escalations: Triage and reproduce product bugs, serving as the bridge between customers and our Engineering teams
Pioneer AI-First Support: Train our AI Agents: Manage the performance of our support bots (Triage, Billing, and Technical), ensuring they provide accurate, high-quality assistance
Curate "AI-Ready" Knowledge: Write and structure documentation specifically optimized for RAG (Retrieval-Augmented Generation), ensuring our AI can easily find and use the right information
Design Human-in-the-Loop Flows: Define the logic for when an AI should hand off to a human, ensuring context is preserved so customers never have to repeat themselves
Monitor & Refine: Review AI responses for accuracy or hallucinations, creating examples of "perfect answers" to continuously fine-tune the model’s performance
Handle Complex "Edge Cases": Step in as the expert "Solutions Architect" for gray-area technical issues where AI reaches its limit
Drive Automation Strategy: Focus on solving problems at the source—building systems and content that resolve issues before a human ever needs to intervene
Requirements:
8+ years of experience in technical support or engineering, with a track record of solving high-complexity SaaS problems
Strong Coding Foundations: Proficiency in Python, JavaScript, or Ruby. You should be comfortable reading SDK source code and reproducing bugs
Modern Dev Stack Knowledge: Deep understanding of CI/CD, APIs, microservices, and observability/monitoring
AI Literacy: Practical familiarity with the AI stack: You understand how RAG works, why Context Windows matter, and how to debug a System Prompt
Exceptional Communication: The ability to explain complex architectural concepts to a human and write precise "system instructions" for an AI
Nice to have:
AI Ops Experience: Prior experience managing Vector Databases or fine-tuning LLMs for production use
Open Source Contributor: Active presence on GitHub or experience maintaining open-source repositories
Former Software Developer: You’ve spent time in the trenches as a full-time dev and know exactly what keeps our customers up at night