Design and operate high-performance datacenter networks for large GPU clusters, ensuring reliable, scalable fabrics for training, inference, storage, and management workloads.
Build and operate high-throughput, reliable storage systems for frontier AI workloads, supporting datasets, checkpoints, and model artifacts as GPU clusters scale while balancing performance, durability, availability, and cost.
Own platform engineering for AI workloads by building secure cloud foundations, reusable infrastructure, reliable delivery workflows, and self-service capabilities that help teams ship and operate services safely.
Build the hosted training platform and Kubernetes infrastructure that lets users launch and monitor fine-tuning runs on managed GPU clusters, connecting developer-facing tools with distributed AI workloads.
This role focuses on building and optimizing large-scale LLM serving and inference systems, enhancing the infrastructure for AI model evaluation and deployment.
Design, deploy, and optimize large-scale GPU infrastructure for customer AI and HPC workloads, from cluster architecture and distributed systems to production troubleshooting and performance tuning.
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