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Lightfield

Founding Support Engineer

HQ: San Francisco$175k–$195kfulltimemidAdded 1 month ago

About this role

Lightfield, an AI-native CRM backed by top-tier VCs, is seeking a founding CS Engineer to build their post-sales function from the ground up. You'll own customer support end-to-end while architecting AI-powered systems—health scoring, automated onboarding, and intelligent agents—that scale support without adding headcount, positioning this role as foundational to the company's growth.

What you'll do

  • Manage inbound support queue across Slack and email, setting SLAs and resolving technical tickets
  • Build and optimize an AI support agent to deflect common questions and keep the knowledge base current
  • Design health scoring models and automated onboarding workflows to drive activation and retention
  • Create internal tooling for customer self-service gaps and document recurring issues as knowledge base content
  • Develop support playbooks, escalation processes, and reporting dashboards for leadership visibility
  • Identify product feedback and expansion opportunities from direct customer interactions

What they're looking for

  • Customer support and ticket triage
  • AI/LLM-powered automation and agents
  • Product documentation and knowledge base management
  • Health scoring and retention analytics
  • Workflow automation and integration debugging
  • Technical troubleshooting and problem-solving
  • Cross-functional collaboration with engineering and product
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Lightfield

Lightfield builds an AI-native CRM that automatically organizes customer interactions and powers sales teams through LLM-driven features and intelligent assistants. The company is hiring AI Product Engineers, Machine Learning Engineers, and Customer Success Engineers to develop full-stack AI capabilities, scale support systems, and shape the technical direction of their product.

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Likely interview questions

  • Walk us through a time you identified a recurring customer problem and built a system or automation to solve it at scale rather than handling each instance manually.
  • How would you approach building and maintaining a knowledge base specifically designed for an AI agent to use, versus one written for human customers?
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