Senior Customer Success Engineer

DigitalOcean • Boston, MA

101d • $87,200 - $130,800

About The Position

Dive in and do the best work of your career at DigitalOcean. Journey alongside a strong community of top talent who are relentless in their drive to build the simplest scalable cloud. If you have a growth mindset, naturally like to think big and bold, and are energized by the fast-paced environment of a true industry disruptor, you’ll find your place here. We value winning together—while learning, having fun, and making a profound difference for the dreamers and builders in the world. We are looking for a Cloud Support Engineer SME with 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 Cloud Support 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.

Requirements

• 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.

Nice To Haves

• 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.

Responsibilities

• 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.

Benefits

• Competitive salary range of $87,200 - $130,800 based on market data, relevant years of experience, and skills.
• Bonus eligibility based on company and individual performance.
• Equity compensation including equity grants upon hire and participation in Employee Stock Purchase Program.
• Reimbursement for relevant conferences, training, and education.
• Access to LinkedIn Learning's 10,000+ courses for continued growth and development.
• Flexible time off policy.
• Employee Assistance Program.
• Local Employee Meetups.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Number of Employees: 1,001-5,000 employees