AI Infrastructure Engineer

You will design AI frameworks for large-scale compute clusters and build distributed training systems for large models. You will optimize GPU utilization, memory use, training throughput, and data-loader efficiency. You will support multimodal large-model and control-model training. You will also build low-latency inference pipelines for real-time robot control using quantization, distillation, and model compilation.

Responsibilities

• Design and build AI frameworks for large-scale compute clusters
• Build and maintain distributed training systems for large-model training
• Optimize GPU utilization, memory consumption, and training throughput
• Optimize data-loader efficiency
• Support training optimization for multimodal large models and control models
• Build low-latency inference pipelines for real-time robot control
• Apply quantization, distillation, and model-compilation techniques to improve inference performance

Requirements

• Experience training on GPU clusters with thousands of GPUs
• Knowledge of distributed training mechanisms such as DDP and FSDP
• Experience with DeepSpeed and Megatron-LM
• Ability to define parallelization strategies based on model structures
• End-to-end performance analysis skills for compute, communication, and data-loading bottlenecks