Director of Customer Success
Job description
About the role
This position builds and leads a customer success motion for a global AI platform company. The director will own end-to-end customer outcomes and develop a high-performing team. Partnership across Sales, Product, Engineering, and Support creates a repeatable customer journey.
Leaders set direction and build the conditions for their teams to succeed. They hire, coach, and hold people accountable. Leadership work spans strategy, communication, and decision making. Effective leaders give clear context and remove obstacles for the people who report to them. Leadership roles are measured by team outcomes, retention, and delivery. Most organizations expect leaders to be visible, predictable, and generous with context.
Key facts
What you'll do
Customer outcomes are owned and a high-performing team of managers and technical account specialists is led for strategic and growth customers.
Customer patterns are translated into roadmap feedback, prioritization signals, and concrete product improvements through close collaboration with Product and Engineering.
At-risk accounts receive careful inspection and customer-facing teams are coached through difficult situations. Stronger long-term relationships are formed by transforming escalations.
Requirements
8+ years in customer success, account management, technical account management, or post-sales leadership, including 3+ years managing teams is mandatory. Experience leading customer success motions for technical, enterprise, or consumption-based products is required.
A strong grasp of retention, expansion, adoption, health scoring, renewal forecasting, and executive stakeholder management is required. Operating strategically while staying close to customers, account details, and team execution is required. Cross-functional experience working with Sales, Product, Engineering, Support, Marketing, and Revenue Operations is required.
Strong communication and executive presence with customers and internal leadership is required. Building systems from scratch in a company that is scaling quickly comes with comfort requirements.
Nice to have
Experience in AI, cloud infrastructure, developer platforms, MLOps, data infrastructure, or enterprise SaaS is a nice-to-have. Usage-based revenue, cloud consumption, or complex technical onboarding experience is a nice-to-have. Building a customer success function through a high-growth stage is a nice-to-have. Familiarity with ML teams, research organizations, platform engineering teams, or infrastructure buying centers is a nice-to-have.
Practical notes
The role reports to the CRO and follows a hybrid work model. Typical interview steps
Leadership hiring usually includes a strategy case, a people scenario, and a values conversation. Candidates may be asked to turn around a struggling team or plan for growth. Past results are examined closely, especially how decisions were made. Interviewers dig into specific decisions you made and their consequences. Preparing two or three decision stories with numbers and lessons is the best use of time.
Good to know
Questions to ask
Useful questions for the interview: what a typical week looks like, how work is assigned, what tools the team uses, and how feedback works. Asking how the role has changed recently and what the team wishes it had known when joining is also reasonable. Questions about the manager's priorities are especially valued.
Career growth
Leadership careers move from manager to senior manager, director, and executive roles. Each step adds more scope and more responsibility for outcomes. The best leaders keep learning how to delegate, communicate, and decide. Leadership careers reward a record of building and developing people. The higher you go, the more your communication shapes the whole organization.
About the company
Lightning AI provides an all-in-one environment for building and deploying machine learning models. You write, test, and refine code directly in the browser, moving from quick experiments to full training runs and serving. The platform handles scaling and deployment so you can focus on model behavior. It is created by the team behind PyTorch Lightning.