Senior Technical Support Engineer

As a Technical Support Engineer, you're the bridge between our customers and our Engineering organization. You'll own technical support cases end-to-end, triaging issues across Kubernetes infrastructure, ML platform components, authentication, data connectivity, and model deployment, and ensuring every customer gets a clear and timely resolution. You'll also contribute to the knowledge base that helps the whole team scale.

• Own support cases for enterprise customers across all severity levels, from initial triage through resolution, with clear communication and accurate expectations throughout
• Diagnose and resolve Kubernetes and cloud infrastructure issues: pod failures, resource limits, persistent volumes, RBAC, ingress, and cluster-level diagnostics
• Troubleshoot ML platform problems including workspace and job failures, environment build errors, model deployment issues, and data connector failures
• File detailed, actionable bug reports and enhancement requests in Jira and act as the customer's advocate with Product and Engineering
• Write and review knowledge base articles, how-to guides, and troubleshooting docs, building the reference layer that helps customers and teammates solve problems faster
• Hand off cases cleanly in a follow-the-sun model across AMER, EMEA, and APAC, ensuring continuity for global enterprise accounts
• Run live troubleshooting sessions with customers via video call and participate in EMEA weekend on-call rotation per team schedule

• 3 to 5 years in enterprise technical support, solutions engineering, or a similar customer-facing technical role at a SaaS or data/AI platform company
• Hands-on Kubernetes: pod lifecycle, kubectl, RBAC, namespaces, persistent volumes, and cluster-level troubleshooting
• Strong Linux and command-line proficiency: log analysis, process management, file system navigation, and shell scripting
• Familiarity with Python-based ML workflows: Jupyter, package management, model training and serving
• Experience with cloud platforms (AWS, GCP, or Azure) and containerized application environments
• Methodical troubleshooter: you form a hypothesis, test it, and adapt when the logs disagree with your theory
• Clear written communicator: your case updates and KB articles don't require a follow-up to understand
• Comfortable managing multiple open, time-sensitive cases without losing the thread on any of them
• Works well asynchronously across time zones in a remote-first, globally distributed team
• Bachelor's degree in computer science, engineering, or a related technical field (or equivalent experience)