Head of Customer Engineering

Responsibilities

• Lead a high-performing Customer Engineering team responsible for technical support, customer escalations, and technical issue resolution.
• Own the operational health of the team, including customer satisfaction, support quality, response times, and engineering effectiveness.
• Coach and mentor highly technical engineers, fostering a culture of technical excellence and customer focus.
• Partner closely with Product Management and Engineering to communicate customer pain points and influence product improvements.
• Guide the team through complex debugging and root-cause analysis involving distributed systems, cloud infrastructure, and AI workloads.
• Drive initiatives to reduce repeat customer issues through improved documentation, diagnostics, and automation.

Requirements

• 8+ years of engineering experience, including 3–5+ years leading Support Engineering, Customer Engineering, SRE, or related technical teams.
• Experience leading customer-facing engineering teams in a high-growth SaaS or infrastructure software company.
• Strong technical background in cloud infrastructure, distributed systems, Kubernetes, or AI/ML platforms.
• Demonstrated success in improving technical customer organizations through operational improvements and cross-functional partnerships.
• Exceptional communication skills with the ability to work effectively with customers, executives, and engineers.
• Entrepreneurial mindset with a strong sense of ownership and a bias for execution.

Preferred Qualifications

• Experience contributing software, tooling, or automation while working in a customer-facing role.
• Experience with Ray or other distributed computing frameworks.
• Experience supporting AI/ML infrastructure or modern cloud platforms.
• Experience working at a Series C or Series D startup.
• Experience working with open-source communities.

About the Company

Anyscale is on a mission to democratize distributed computing. We are commercializing Ray, a popular open-source project that creates an ecosystem of libraries for scalable machine learning. Companies like OpenAI, Uber, and Spotify use Ray to accelerate the progress of AI applications. We are building the best place to run Ray, allowing developers to scale ML applications from a laptop to a cluster seamlessly.