Startups · AI

Customer Support Engineer

Pocket · San Francisco, CA, US · On-site

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About Pocket

Take Notes in the Real World. Backed by Accel and Y Combinator.

About the role

The engineer behind customer support. We run a self-hosted helpdesk with an AI agent on top of it, and you own that stack: the automations that take repetitive work off the team, the integrations that put order and device data in the ticket, the pipelines and dashboards that show where volume comes from. A growing share of the work is enterprise.

What they're looking for

  • You've owned a service in production end to end
  • Strong with TypeScript and SQL, and comfortable running and debugging a self-hosted app
  • SQL well past the basics, and enough Python to do the analysis yourself
  • You've built dashboards people use
  • Hands-on with LLM tooling - prompts, retrieval, evals. You can tell why an AI agent gave a bad answer and fix it
  • You've built for business customers and you know what changes at that tier: admin roles, audit trails, SLAs, single sign-on, data handling that survives a security review
More about this role

The engineer behind customer support. We run a self-hosted helpdesk with an AI agent on top of it, and you own that stack: the automations that take repetitive work off the team, the integrations that put order and device data in the ticket, the pipelines and dashboards that show where volume comes from. A growing share of the work is enterprise. We sell Pocket into companies now, and B2B support runs on different rails - named accounts, SLAs, admin tooling, security reviews - and most of that layer doesn't exist yet. This is a hands-on role. You sit with the support team every day and you ship your own code.

  • Our self-hosted helpdesk: uptime, upgrades, configuration, and the patches we contribute back upstream
  • Automations and integrations that remove manual steps - routing, macros, ticket enrichment from our order, fulfillment, and device systems
  • The AI support agent and the knowledgebase behind it: retrieval quality, evals, and pushing resolution rate up
  • The support data layer: pipelines, models, and dashboards covering volume, contact drivers, deflection, CSAT, and team performance
  • Analysis that turns ticket data into decisions - what's breaking, what it costs, what...

Read the full posting on Pocket's site ↗

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