Technical Support Engineer

Job description

About Kustomer Kustomer is the AI-native customer experience platform that turns every interaction into a lasting relationship and a measurable business outcome. By unifying customer data, conversations and AI-native workflows into a single connected timeline, Kustomer helps teams cultivate customers, not tickets, resolving issues faster and delivering more personalized service across every channel. Today, Kustomer is the core platform for leading customer service brands like Sweetgreen, Starz, Vuori, Goldbelly and Cotopaxi. Founded in 2015 by serial entrepreneurs Brad Birnbaum and Jeremy Suriel, Kustomer raised nearly $200M in funding culminating in a Series E before being acquired by Meta in a deal that closed in 2022. In 2023, Kustomer's founders and original investors, Battery, Redpoint, and Boldstart Ventures, spun the company back out with a $60M investment as an independent, privately held business ready to focus more deeply on AI. In 2025, Norwest led our Series B, bringing our total funding to $90M. This momentum allowed Kustomer to double down on AI-native innovation and build a sharper roadmap for the future of customer experience. Our Krew is a remote-friendly team spread across the U.S. with hubs in NYC and London. We gather in person each year at our annual Kustomer Kick-Off event and always look for Krew members who are curious, collaborative, and ready to shape the future of Customer Experience. About the Role Kustomer’s AI agent resolves routine questions, and our support partners handle the next layer of straightforward requests. This role handles what’s left: complex, ambiguous technical issues across webhooks, APIs, workflows, integrations, and AI agent behavior. You’ll be the final escalation point before Engineering. You’ll also make Tier 1 and 2 stronger by turning each hard case into a runbook, a fix, or a product improvement. You’ll be responsible for: Owning technical issues end to end, from reproduction through root cause and resolution, within defined SLAs Diagnosing failures in webhooks, REST API integrations, workflows, and automations using logs, payloads, and developer tools Troubleshooting AI agent behavior, such as misrouted or incorrect resolutions, knowledge gaps, and failed actions or integrations, and recommending configuration or knowledge fixes Escalating to Engineering with clear reproduction steps, evidence, and imp…