Member of Technical Staff AI Training Infrastructure

You will design, build, and optimize infrastructure for large-scale model training. You will develop distributed training pipelines, optimize workloads across GPUs and data centers, manage training data storage, automate provisioning and orchestration, implement observability tools, and troubleshoot performance issues.

Responsibilities

• Design and implement scalable infrastructure for large-scale model training
• Develop and maintain distributed training pipelines for LLMs and multimodal models
• Optimize training performance across GPUs, nodes, and data centers
• Implement monitoring, logging, and debugging tools
• Architect and maintain storage for training datasets
• Automate infrastructure provisioning, scaling, and orchestration
• Analyze and improve training-system efficiency, scalability, and cost-effectiveness
• Troubleshoot distributed training performance issues

Requirements

• 3+ years of experience with distributed systems and ML infrastructure
• Experience with PyTorch
• Proficiency with AWS, GCP, or Azure
• Experience with Kubernetes and Docker
• Knowledge of data parallelism, model parallelism, and FSDP

Benefits

• Equity