Set the technical vision and define the roadmap for frontier AI infrastructure spanning a multi-cloud heterogeneous compute fleet Design and build distributed systems for job scheduling, cluster management, and cost governance Partner closely with ML researchers and engineers to scale and optimize end-to-end performance of AI training and inference workloads Establish connections with cross-functional partners, collaborating with teams across cloud infrastructure, business operations, finance, and engineeringBachelor's degree in Computer Science, relevant technical field, or equivalent practical experience 15 years of experience designing and building very large-scale distributed systems Proficiency in at least 1 backend language (e.g., Python, C++, Go, Rust) Proficiency in cloud-native architectures and compute orchestration platforms (e.g., Kubernetes) Hands-on experience working with ML accelerators such as GPUs and TPUs Track record of mentoring senior engineers and setting long-term architectural vision across orgsMaster's degree or PhD in Computer Science or related technical fields Experience in running distributed training and/or inference workloads in production Expertise in ML systems performance profiling, debugging, and optimization Expertise in deep learning frameworks (e.g., PyTorch, JAX) and their underlying architectures. In this role, you will shape the architecture and long-term technical strategy for large-scale training and inference systems, working at the intersection of AI research, systems, and cloud infrastructure.