Senior Site Reliability Engineer, Platform Infrastructure

Responsibilities

• Design, build, and scale services that orchestrate Ray clusters across cloud and on-prem environments, supporting both VM-based and Kubernetes-based deployments
• Optimize control plane components for large-scale, distributed AI/ML workloads
• Build intelligent scheduling and resource management systems for heterogeneous compute clusters
• Develop features to enhance the reliability, performance, scalability, and observability of Anyscale-managed Ray workloads
• Support and optimize accelerator integration (e.g., GPUs, TPUs)
• Handle container image management and dependency resolution for distributed workloads
• Participate in code reviews, design and architecture discussions
• Provide on-call support, working closely with customer and field teams to troubleshoot infrastructure issues
• Collaborate with leading distributed systems and machine learning experts to push the boundaries of AI infrastructure

Requirements

• Bachelor's degree in Computer Science, Engineering, or equivalent practical experience
• 3+ years of experience writing high-quality production code
• Hands-on experience in building and maintaining highly available, scalable, and performant distributed systems
• Expertise in cloud-native technologies (AWS, Azure, GCP) and Kubernetes-based deployments
• Deep understanding of networking, security, and authentication mechanisms in cloud environments
• Familiarity with observability stacks (Prometheus, Grafana, etc.)
• Proficiency in Go and Python
• Knowledge of low-level operating system foundations (Linux kernel, file systems, containers)