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About the Role

DigitalOcean is seeking a Manager, Customer Success Engineering (AI & Cloud Support) passionate about delivering exceptional support experiences and building high-performing teams. This role involves leading a team of Customer Success Engineers (CSEs) supporting strategic customers across cloud infrastructure and emerging AI/ML workloads, owning day-to-day support operations, team performance, and customer experience outcomes. The position focuses on driving operational excellence and building technical depth in AI, GPUs, Kubernetes, Databases, and core cloud offerings, while partnering with Product and Engineering to enhance customer experience and product quality.

Responsibilities

Lead, hire, train, mentor and develop a high-performing team of Customer Success Engineers (CSEs), driving accountability, performance, and career growth.
Establish performance metrics (KPIs/SLAs) and conduct regular 1:1s, performance reviews, and career development planning.
Own end-to-end support operations, including queue management, escalations, and shift planning to ensure consistent 24x7 coverage.
Drive improvements in key support metrics such as CSAT, response times, resolution times, and overall support quality.
Build and strengthen technical expertise within the team across core areas such as Kubernetes (DOKS), Databases, Compute, and AI/ML workloads.
Act as the ultimate point of technical escalation for our largest, most strategic enterprise customers across Cloud and AI/ML workloads, stepping in to manage critical incidents and high-severity (Sev1/Sev2) issues.
Design and implement customized support plans, SLAs, and escalation pathways tailored to the needs of strategic accounts.
Partner closely with Technical Account Managers (TAMs), Growth Account Managers (GAM) to conduct Executive Business Reviews (EBRs) and ensure customers are maximizing the value of our Cloud and AI/ML products.
Proactively identify risks and opportunities within strategic accounts to improve customer experience, adoption, and retention.
Serve as the Voice of the Customer (VoC) to Product and Engineering teams, synthesizing support data to advocate for bug fixes, feature requests, and UX improvements.
Own and continuously improve escalation protocols between AI/ML Support and CloudOps, Infrastructure Engineering, and Product — including Jira escalation routing, Sev1 bridge management, and post-incident documentation.
Own the development and maintenance of SOPs, escalation runbooks, HVC support playbooks, and knowledge base content — treating documentation infrastructure as a core operational lever for team scalability.
Contribute to the vision for AI and automation within support—building intelligent tooling and driving the team toward an automation-first model to improve efficiency, scalability, and customer experience.
Foster a culture of continuous learning, ensuring the team stays ahead of evolving cloud technologies, AI/ML frameworks, and industry trends.

Requirements

5+ years of experience in Technical Support, Customer Success, or Technical Account Management within B2B SaaS, Cloud, or AI/ML environments ideally including experience supporting AI-native, high-growth companies with 24x7 production dependencies on GPU infrastructure.
2+ years of people management experience leading technical, customer-facing teams, preferably in a high-growth, post-acquisition, or rapidly scaling environment.
Solid understanding of AI/ML concepts, including Generative AI, Large Language Models (LLMs), natural language processing (NLP), and MLOps.
Deep familiarity with GPU infrastructure (NVIDIA H100/H200, bare metal GPU provisioning) and AI inference workloads is strongly preferred.
Proficiency in reading and debugging code (Python preferred) and troubleshooting RESTful APIs and cloud architecture.
Excellent verbal and written communication skills, with the ability to translate complex technical or AI concepts for diverse audiences OR to both highly technical engineers and non-technical business executives.
Proven ability to remain calm under pressure and de-escalate high-stakes situations with enterprise clients.
Hands-on experience with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and AI toolchains (e.g., LangChain, Hugging Face).
Experience with major cloud platforms (AWS, Google Cloud, Azure) and their native AI/ML services.
ITIL or equivalent service management certification.

Skills

AI/ML concepts
Generative AI
Large Language Models (LLMs)
Natural Language Processing (NLP)
MLOps
GPU infrastructure
NVIDIA H100/H200
Bare metal GPU provisioning
AI inference workloads
Python
RESTful APIs
Cloud architecture
TensorFlow
PyTorch
Scikit-learn
LangChain
Hugging Face
AWS
Google Cloud
Azure
Customer Satisfaction (CSAT)
Time to Response
Resolution Time
SLA adherence
Escalation management
Technical Support
Customer Success
Technical Account Management
Team Leadership
Mentoring
Performance Management
Cross-functional Collaboration
Voice of the Customer (VoC)
Documentation
Automation

Location

Remote

Work Type

Remote
Full-time

Experience Level

5+ years of experience in Technical Support, Customer Success, or Technical Account Management
2+ years of people management experience

Education Level

Bachelor's or Master’s degree in Computer Science, Data Science, Engineering, or a related technical field.

Salary/Compensations

$125,000 - $153,000

Benefits

Employee Assistance Program
Local Employee Meetups
Flexible time off policy
Reimbursement for relevant conferences, training, and education
LinkedIn Learning access
Bonus potential
Equity compensation
Employee Stock Purchase Program

About the Company

DigitalOcean is a cutting-edge technology company with an upward trajectory, proud to simplify cloud and AI so builders can spend more time creating software that changes the world.
We are a high-performance organization that will always challenge you to think big.
Our organizational development team will provide you with resources to ensure you keep growing.

Equal Opportunity

DigitalOcean is an equal-opportunity employer. We do not discriminate on the basis of race, religion, color, ancestry, national origin, caste, sex, sexual orientation, gender, gender identity or expression, age, disability, medical condition, pregnancy, genetic makeup, marital status, or military service.