Senior Site Reliability Engineer - AI Infrastructure

Role Overview

Design, operate, and debug large-scale GPU infrastructure used for distributed training and inference, working directly with customers pushing the limits of modern AI systems.

What You Will Do

Design and evolve multi-provider, multi-region GPU compute clusters, serve as primary technical point of contact for customers, define SLOs and error budgets, ensure health and performance of high-speed interconnects, build deep visibility into GPU utilization, and lead incident response for complex failures.

Why It Might Be a Fit

You will have significant ownership and autonomy to shape how our systems run at a foundational level, working directly with customers and providers while architecting the infrastructure backbone for reliable, scalable AI compute.

Requirements

• Deep, hands-on experience operating large-scale GPU clusters
• Production experience with InfiniBand, RoCE, or NVLink fabrics in the context of distributed training
• Working knowledge of how large training jobs actually run — NCCL, CUDA, PyTorch distributed, DeepSpeed, Megatron, FSDP, or similar
• Expert-level Linux knowledge: kernel tuning, driver management, cgroup/namespace internals, performance profiling at the syscall and hardware level
• Strong experience running Kubernetes in production with GPU workloads, including device plugins, topology-aware scheduling, multi-cluster federation, and custom operators
• Strong engineering skills in Python, Go, or Bash, and Infrastructure-as-Code proficiency (Terraform, Helm, Ansible, or equivalent)
• Hands-on experience building monitoring and alerting for GPU infrastructure, and proven track record leading incident response for complex distributed systems

Benefits

• Equal opportunity employer
• Inclusive environment for all employees
• Autonomy to shape how systems run at a foundational level
• Significant ownership and impact
• Opportunity to work directly with customers and providers