Leads and scales technical support team for AI Sequencing product, managing KPIs across chat/email/Slack, building AI automations/knowledge base, and driving cross-functional fixes with Eng/PM. Requires 5+ years technical support with 2+ in leadership at B2B SaaS startups.
154k – 206k/yr
Hybrid5+ YOESupport Engineering
About the role
Responsibilities
Lead and scale a team of veteran support ICs (8+ years average). Hold the bar high: run weekly QA on responses, take hardest escalations, uplevel through hands-on review.
Own day-to-day support outcomes across channels (live chat, Slack, email), ensuring SLAs and KPIs (first response time, resolution time, CSAT) are exceeded.
Design AI-native shared knowledge base for fast-growing product.
Build agents and automations to automate repetitive work, decoupling team growth from ticket volume.
Represent voice of customer; find patterns/trends across tickets and collaborate with PM/Eng to ship fixes.
Required Experience
5+ years in Technical Support, including 2+ years in leadership/management at B2B SaaS company.
Experience at early-stage/high-growth startup building/improving support processes.
Proven success managing support KPIs, SLAs, CSAT in real-time live chat (in-app, Slack).
Strong troubleshooting across APIs, MCPs, integrations, VoIP.
Experience leading cross-functional initiatives with Engineering/Product in fast release cadences.
Data-driven; hands-on with support analytics for forecasting, root-cause, decisions.
Preferred Experience
Shipped AI tooling in support (evals, retrieval, agent workflows closing tickets).
Supported AI agent products (prompts, sequences, deliverability, agent eval).
Worked with modern support stacks (Pylon, Intercom, Zendesk).
Familiar with sequencers (Outreach, Apollo, Salesloft) from buyer side.
Built knowledge base that deflected tickets (with analytics).
Skills
AI AgentsAPIsIntegrationsVoipSlackIntercomZendeskOutreachApolloSalesloftSupport AnalyticsAgent WorkflowsPromptsSequencesKnowledge Base
Support Operations Engineer owns customer interactions end-to-end, identifies recurring issues for product/engineering improvements, maintains AI-powered knowledge bases and support tooling, and analyzes data for operational enhancements in a B2B tech environment.
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