Support Engineer, AI Infrastructure

Figma is growing our team of passionate creatives and builders on a mission to make design accessible to all. As a Support Engineer, AI Infrastructure, you will design, build, and operationalize integrations that enhance support tools and workflows, applying AI to improve the customer and Specialist experience.

Responsibilities:

• 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 relevant customer, account, product, billing, file, or admin metadata into support conversations so chatbots and Specialists have the context they need to resolve issues more effectively
• 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 to align on requirements, implementation, governance, and rollout
• Build quality checks, monitoring, fallback paths, and operational guardrails so AI-powered workflows can be trusted in production
• Define success metrics for each workflow, track adoption and impact, and iterate based on customer outcomes, Specialist efficiency, and adoption

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 (e.g., Ruby, Python, Go, C++, PostgreSQL), with 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/support experiences, including working with operational data to debug issues, improve workflows, and measure impact
• Strong product and stakeholder instincts: you can translate ambiguous support problems into practical, adopted, and measurable technical solutions
• Proven track record of designing AI workflows with clear guardrails, fallback paths, and responsible deployment practices
• Experience with support platforms like Zendesk, Decagon, Sprinklr, Gainsight, Maestro QA/Rippit, Assembled, Salesforce, or similar systems
• Familiarity with agent assist tooling, AI support chatbots, copilot tooling, RAG, AI observability, or monitoring AI workflows in production
• Experience building internal Slack tooling, workflow automations, or embedded support experiences
• Background in Support Engineering, Internal Tools Engineering, Solutions Engineering, Support Operations, CX Systems, or Business Systems
• Familiarity with customer support metrics such as containment, deflection, CSAT, first contact resolution, routing accuracy