Design, build, and operate large-scale GPU infrastructure for high-throughput inference and mid-training workloads at an AI lab. Day-to-day work spans distributed systems for synthetic data generation and reinforcement learning, optimizing model execution and GPU utilization (SGLang, Megatron, kernel optimization), and resolving performance bottlenecks across runtimes, networking, and distributed
You will design, build, and operate GPU infrastructure for high-throughput inference and mid-training workloads. You will develop distributed systems for synthetic data generation and reinforcement learning, optimize model execution and GPU utilization, support large-scale evaluations, and resolve performance bottlenecks across runtimes, kernels, networking, and distributed compute.
Responsibilities
Design build and operate large-scale GPU infrastructure
Develop systems for synthetic data generation and reinforcement learning pipelines
Build high-performance inference platforms across thousands of GPUs
Optimize inference throughput latency and GPU utilization
Support distributed reinforcement learning and model evaluation workloads
Improve model execution through kernel optimization model parallelism and GPU runtime improvements
Diagnose and resolve performance bottlenecks across distributed systems