Build and maintain automated testing frameworks for distributed AI training and inference workloads. Develop end-to-end integration and stress-testing plans, validate GPU and hardware-software interactions, and troubleshoot failures in large-scale distributed systems. Partner with system engineers using telemetry and profiling tools to identify bottlenecks. The role requires Python, test automation, containerization, CI/CD, and familiarity with infrastructure management tools.

Design and implement system software, communication primitives, and execution primitives for large-scale AI workloads across thousands of GPUs. Analyze and resolve distributed training and inference performance bottlenecks, investigate software-hardware interactions, and benchmark multi-node clusters. Collaborate across the technology stack to shape foundational AI infrastructure. Required skills include C++, Python, systems programming, and Linux; GPU programming and distributed AI framework experience are preferred.