Infrastructure Engineer ML Compute

You will own GPU compute for model training, evaluation, and serving. You will operate Kubernetes clusters, GPUs, and schedulers; improve scheduling and placement; maintain node health; manage cloud capacity; and debug infrastructure failures across Linux, storage, networking, and GPU hardware.

Responsibilities

• Run Kubernetes clusters for GPU training and inference
• Improve GPU workload scheduling and placement
• Detect, drain, and repair unhealthy GPU nodes
• Manage capacity with cloud providers
• Debug failures in GPUs, Linux, storage, and networking

Requirements

• Build and run GPU workloads on Kubernetes
• Design GPU clusters for training and online reinforcement learning
• Debug Linux, GPU drivers, networking, and storage
• Write Python