Support Software Engineer

Role overview

A post-launch engineering role on a Technical Support team responsible for keeping AI-powered customer service agents running reliably in production. The position blends investigation, hands-on coding, and direct customer communication, with success measured by reliability and resolution rather than feature delivery. It spans a broad technical surface including conversational AI behavior, voice and telephony, integrations, authentication, and legacy TypeScript codebases.

Responsibilities

- Own bugs, issues, and enhancement requests for live bots and products, taking escalations that frontline support cannot resolve on their own - Investigate root causes across logs, code, and internal tooling, then design, implement, test, and ship fixes - Maintain a holistic view of platform health across AI agent behavior and configuration, conversation intelligence, telephony, SSO and authentication, integrations, API issues, LLM prompting, and legacy systems - Serve as the primary point of contact for customers on live issues, gathering requirements, confirming reproduction steps, and coordinating testing directly with them - Act as a technical escalation point for frontline support engineers, providing context and guidance on issues they cannot close alone - Build and maintain internal tools that make the next investigation, fix, or enhancement faster than the last one - Relay customer feedback to the Product team while meeting team SLAs and support metrics

Requirements

- Strong debugging depth, including reading unfamiliar code, tracing logs, and reasoning across distributed systems, REST APIs, and authentication protocols such as OAuth 2.0 - Fluency with agentic coding tools (e.g., Claude Code, Cursor, or equivalents) used as the default approach to investigating and fixing issues, not as an add-on - Comfort maintaining legacy TypeScript code where it is still what runs in production - Customer-facing communication skills, able to gather requirements, confirm reproduction, coordinate tests, and explain fixes in plain language, including with frustrated stakeholders

Nice to have

- Experience with LLM prompting or debugging conversational AI and voice systems - Familiarity with enterprise tools such as Salesforce, Zendesk, or Jira - Background building internal tooling or automations that reduced manual engineering effort

Benefits and work setup

- Remote-first distributed team with company-wide offsites and smaller team gatherings - Health and wellness coverage including flexible vacation, a paid sabbatical after five years, and a stipend for physical and mental well-being - Competitive base compensation plus equity participation - Tech and learning stipend covering conferences, books, and courses