Anyscale
Machine Learning Engineer, Customer Engineering
About this role
Anyscale seeks a Customer Support Engineer to guide enterprise customers through onboarding and adoption of its distributed computing platform, troubleshooting complex technical issues and collaborating with engineering teams. The role combines post-sale customer success with deep technical expertise in ML/AI infrastructure, requiring someone to own issues end-to-end while influencing product improvements.
What you'll do
- Resolve customer technical issues and support successful adoption of the Anyscale platform
- Own customer problems from troubleshooting through escalation and resolution
- Participate in follow-the-sun support model for high-priority ticket continuity
- Track and communicate updates on customer-reported bugs and feature requests
- Collaborate cross-functionally with product and engineering teams on customer feedback
- Build and maintain technical relationships with key customer stakeholders
What they're looking for
- Distributed ML infrastructure and cloud platforms (AWS/GCP/Azure)
- LLM training, fine-tuning, and serving experience
- Kubernetes and container orchestration
- Data pipeline development
- Technical troubleshooting and debugging
- Cross-functional collaboration
- Strong communication and mentoring abilities
- Ray framework experience (bonus)
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Anyscale
Anyscale builds Ray, an open-source distributed computing framework and enterprise platform for scaling AI workloads across Kubernetes and cloud providers. The company is hiring forward-deployed engineers to work embedded with customers, software engineers to develop Ray Core, LLM inference specialists, and customer support engineers who combine technical expertise with post-sale success.
- Website
- anyscale.com
Likely interview questions
- Walk us through a complex distributed ML infrastructure issue you debugged. How did you approach troubleshooting and what was your collaboration process with engineering teams?
- Describe your experience optimizing ML workloads on cloud platforms like AWS/EKS, GCP/GKE, or Azure/AKS. What performance bottlenecks have you identified and resolved?