Customer Experience Engineer

HackerRank helps companies like NVIDIA, Amazon, and Microsoft hire and upskill the next generation of developers based on skills, not pedigree. Our platform is trusted by over 2,500 of the world’s most innovative companies to build strong engineering teams ready for what’s next.

Software has entered an era where humans and AI build side by side. As this shift accelerates, the definition of strong technical talent is changing. We give companies better ways to identify and invest in next-generation skills.

People at HackerRank care deeply about the impact of their work and sweat the small details so our customers can be wildly successful with products they genuinely love to use. We move with urgency and believe great outcomes come from high standards.

About the role

Customer Experience Engineering is where customers, product, and engineering meet. As a CXE 2, you’ll be trusted to lead complex, ambiguous, high-risk, and enterprise customer problems end to end. You’ll design investigations where no playbook exists, connect behaviour across systems, make sound decisions under pressure, and hold the thread across customers, Engineering, Product, and GTM until the outcome is complete.

This is not a traditional Support role. CXE 2s do not wait for ‘level-appropriate’ tickets or hand work away after identifying a bug. You’ll share responsibility for one CXE queue, lead the situations where experience matters most, and convert recurring problems into tooling, AI-enabled workflows, product action, and prevention.

What you will do

• Lead complex, ambiguous, enterprise, solution-led, and escalated customer issues from investigation through resolution and trust-restoring closure.
• Design and execute investigations across web applications, APIs, integrations, authentication, data, logs, configurations, and system behaviour, even when no playbook exists.
• Resolve the majority of technical issues independently and engage Engineering only with clear evidence, reproduction steps, impact, and a strong technical hypothesis.
• Lead high-severity incident response and customer-facing RCAs, driving containment, stakeholder alignment, proactive communication, corrective action, and follow-through.
• Manage multiple concurrent priorities while protecting the health of the shared queue, service levels, and communication quality across shifts and regions.
• Use AI and coding tools to accelerate debugging, analyse patterns, build or maintain reliable agents and automations, and contribute lightweight code or tooling fixes.
• Turn recurring issues into adopted runbooks, onboarding improvements, evaluation loops, automation, product feedback, or Engineering action that reduces repeat work.