Member of Technical Staff (AI Infrastructure Engineer)

Highlights

• Build and optimize large-scale clusters for AI training and inference.
• Partner with research and inference teams to support critical workloads.
• Improve performance and uptime for distributed LLM training and serving.

Ideal candidate

Systems-minded infrastructure engineer with 3-5 years supporting ML platforms who thrives on reliability, performance tuning, and partnering with researchers to keep large-scale training and inference running smoothly.

Requirements

• Expertise in Kubernetes administration including CRDs, operators, and cluster management
• Hands-on experience with Slurm job scheduling, resource allocation, and optimization
• Experience deploying and managing distributed training systems at scale
• Deep understanding of container orchestration and distributed systems architecture
• Familiarity with LLM architecture and distributed training strategies
• Experience managing GPU clusters and optimizing compute utilization
• Proficiency in Python and C++ for systems and infrastructure automation
• Experience with PyTorch in distributed training contexts
• Understanding of networking, storage, and compute for ML workloads
• Experience building APIs and orchestration for training and inference services
• 3-5 years managing large-scale Kubernetes and Slurm environments in production