Senior Customer Success Engineer I

We are looking for a Senior Customer Success Engineer SME with a minimum 5 years of experience and deep expertise in Kubernetes, AI/ML workloads, and GPU infrastructure who is passionate about helping our customers scale and innovate with cutting-edge technologies.

As a Senior Customer Success Engineer at DigitalOcean, you will join a dynamic team dedicated to revolutionizing cloud computing and AI. You will be a key member of our advanced support team, helping DigitalOcean's strategic customers succeed by providing white-glove support and guidance across complex technical domains. Reporting to the Manager of Customer Success Engineering, you’ll act as both a trusted advisor and a technical troubleshooter, empowering our customers to build and scale confidently on our platform.

• Act as a technical subject matter expert (SME) for Kubernetes, AI/ML workloads, and GPU-backed infrastructure.
• Troubleshoot and resolve advanced support cases related to orchestration, performance tuning, container networking, and GPU-based compute.
• Engage directly with our strategic and high-value customers via tickets, Slack, email, and live sessions.
• Partner with Engineering and Product teams to provide feedback on platform usability, bugs, and customer needs.
• Help shape internal runbooks, SOPs, and documentation to scale AI/ML and GPU-related support.
• Participate in incident management, root cause analysis, and retrospective reviews.
• Contribute to the architecture and optimization of customer workloads for high availability and cost efficiency.
• Educate and mentor internal team members on Kubernetes and GPU-based architectures.
• Influence roadmap priorities by surfacing recurring pain points and opportunities.

What You’ll Add to DigitalOcean:

• 5+ years in technical support, DevOps, solutions engineering, or SRE roles.
• Deep experience with Kubernetes (preferably CKA-certified) in production environments.
• Experience supporting AI/ML workflows using GPUs (e.g., NVIDIA A100, L4, CUDA, TensorFlow, PyTorch).
• Familiarity with container lifecycle management, GPU scheduling, and scaling AI jobs in Kubernetes.
• Advanced knowledge of Linux systems administration (Ubuntu/Debian), shell scripting, and performance tuning.
• Deep Knowledge of Bare Metal and Virtualized environments
• Ability to communicate complex technical topics clearly to customers and cross-functional stakeholders.
• Experience troubleshooting full-stack deployments—containers, orchestration, networking, and storage.
• Comfortable working independently and collaboratively in a remote environment.

• Familiarity with cloud-native observability stacks (Prometheus, Grafana, OpenTelemetry).
• Hands-on experience with Paperspace, JupyterHub, Kubeflow, or Ray.
• Exposure to networking topics like CNI plugins, overlay networks, and ingress controllers.
• Prior experience in customer-facing roles at IaaS/PaaS providers or ML Ops platforms.

JR: [2026-7644]

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