Support Engineer, AI Infrastructure & Tooling

About the role

Figma seeks a Support Engineer on its AI Infrastructure and Tooling team to build the technical systems that connect support tools, customer and account context, internal systems, and AI workflows. The role designs and operationalizes integrations across support platforms, applies LLM patterns to customer-support workflows, adds monitoring and guardrails, partners cross-functionally on implementation and rollout, and measures adoption and operational impact. It is full-time and may be based at a US hub or remotely in the United States.

Requirements

• 3+ years of experience shipping integrations, automations, or internal tools across customer-facing operational systems
• Strong proficiency in modern back-end technologies and languages such as Ruby, Python, Go, C++, or PostgreSQL
• Hands-on experience building APIs, implementing webhooks, orchestrating data flows, and integrating systems across complex workflows
• Hands-on experience with LLM-powered workflows, AI automations, or AI-enabled customer or support experiences
• Experience using operational data to debug issues, improve workflows, and measure impact
• Ability to translate ambiguous support problems into practical, adopted, and measurable technical solutions
• Track record of designing AI workflows with guardrails, fallback paths, and responsible deployment practices

What you'll do

• Build and operationalize AI-powered workflows that improve Product Support experiences for customers and internal support teams
• Design and maintain integrations across Decagon, Zendesk, Figma admin tooling, internal data sources, and adjacent Product Support platforms
• Bring customer, account, product, billing, file, or admin metadata into support conversations for better issue resolution
• Use LLMs and AI patterns for classification, summarization, routing, recommendations, context enrichment, and workflow automation
• Partner with Engineering, Analytics, Security, Programs, Support, and vendor teams on requirements, implementation, governance, and rollout
• Build quality checks, monitoring, fallback paths, and operational guardrails for trusted production workflows
• Define workflow success metrics, track adoption and impact, and iterate based on customer outcomes and Specialist efficiency

Where this was posted

• job-boards.greenhouse.io

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