Support AI Engineer

Figma•San Francisco, CA

21d•$140,000 - $202,000•Hybrid

About The Position

Figma is growing its team of passionate creatives and builders on a mission to make design accessible to all. Figma’s platform helps teams bring ideas to life—whether you're brainstorming, creating a prototype, translating designs into code, or iterating with AI. From idea to product, Figma empowers teams to streamline workflows, move faster, and work together in real time from anywhere in the world. If you're excited to shape the future of design and collaboration, join us! Figma is evolving the Product Support experience, powered by AI, automation, and integrated systems. The AI Infrastructure & Tooling team helps make that possible by building intelligent, resilient, and integrated solutions that automate workflows, connect systems, and streamline support operations. As a Support AI Engineer on this team, you'll be the technical execution layer that brings our support tools, customer and account context, internal systems, and AI workflows together. You'll design, build, and operationalize integrations across systems like Decagon, Zendesk, Figma admin tooling, and adjacent Product Support platforms. Your work will help us bring the right context into customer conversations, automate complex workflows, and optimize both the customer and Specialist experience by applying AI where it can meaningfully improve support workflows, quality, and efficiency. This role is ideal for someone who can move from ambiguous support problems to working technical solutions: understanding the workflow, identifying the systems involved, building the integration or automation, validating the data flow, and measuring the impact on customer outcomes and Specialist efficiency.

Requirements

3+ years of experience shipping integrations, automations, or internal tools across customer-facing operational systems.
Strong coding or scripting ability, including experience with APIs, webhooks, data flows, and system and workflow data integrations.
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.

Nice To Haves

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.

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.