Moonlite

• Collaborate with infrastructure to design and build scalable SDN orchestration systems leveraging NVIDIA Bluefield-3 DPUs to deliver programmable, high-performance networking for AI workloads with hardware-accelerated forwarding isolation. • Design and implement networking systems for research computing environments including Kubernetes and SLURM clusters, enabling high-performance connectivity, optimized network topology for distributed workloads, and seamless integration with cluster orchestration systems. • Implement automated SDN provisioning systems that handle VPC creation, subnet allocation, routing configuration, and network resource lifecycle from deployment through decommissioning. • Develop platform capabilities for managing Bluefield-3 DPUs including SR-IOV virtual function management, OVS offload configuration, network function deployment, and integration with compute orchestration systems. • Build enterprise-grade network isolation using VPCs, VXLAN, and hardware-accelerated forwarding to ensure complete tenant separation while maintaining high-performance connectivity for GPU clusters and distributed workloads. • Collaborate with infrastructure to optimize network paths for RDMA, RoCE, and GPU-to-GPU communication, ensuring minimal latency and maximum throughput for distributed training and large-scale computational workloads. • Develop robust APIs and SDKs for network resource management that integrate seamlessly with compute and storage platforms, enabling programmatic network provisioning and configuration. • Implement comprehensive network monitoring, telemetry, and troubleshooting systems that provide visibility into network performance, utilization, and tenant traffic patterns. • Build platform network security features including security groups, firewall rules, and policy enforcement that protect tenant workloads while enabling flexible network configuration.

• Design and build scalable compute orchestration platforms that manage GPU clusters, bare-metal server provisioning, and resource allocation across co-located infrastructure environments. • Implement intelligent workload scheduling, resource allocation, and optimization algorithms that maximize GPU utilization while maintaining performance guarantees for research and training workloads. • Design and implement systems for provisioning and managing research computing environments including Kubernetes and SLURM clusters, enabling automated deployment, resource scheduling, and workload orchestration for distributed AI training and HPC workloads. • Develop platform capabilities for managing latest-generation NVIDIA GPU configurations (H100, H200, B200, B300), including GPU resource management, multi-tenant isolation, and integration with compute orchestration systems. • Build automation and tooling for complete bare-metal server lifecycle management – from initial provisioning and configuration through ongoing operations, updates, and resource reallocation. • Optimize compute platform components for high-throughput and low-latency performance, ensuring research workloads achieve near-bare-metal efficiency in virtualized or containersized environments. • Develop robust APIs and SDKs that enable researchers to programmatically provision and manage compute resources, integrating seamlessly with existing workflows and research infrastructure. • Implement comprehensive monitoring and telemetry systems for compute resources, providing visibility into GPU virtualization, workload performance and infrastructure health. • Build enterprise-grade multi-tenant compute isolation, security boundaries, and resource quotas that enable safe sharing of GPU infrastructure across teams and organizations.

• Design and build systems that bridge physical infrastructure with customer-facing services • Design and implement systems for provisioning and managing research computing environments • Implement comprehensive orchestration systems for various workloads • Design and build network provisioning automation • Develop robust APIs and SDKs for managing infrastructure resources • Implement comprehensive observability, telemetry, and logging systems • Build and optimize platform services for high-throughput low-latency networking • Work closely with engineering, infrastructure, and product teams • Implement platform-wide compliance and security features

• Design, build, and operate production Kubernetes clusters on bare-metal infrastructure. • Implement and operate custom Kubernetes networking solutions. • Develop and maintain custom Kubernetes operators and controllers. • Deploy and optimize NVIDIA GPU operators and custom scheduling logic for GPU workloads. • Build deep integrations between Kubernetes and underlying infrastructure. • Design and implement automation using Terraform, Ansible, Helm, and custom operators. • Manage production bare-metal infrastructure across multiple regions ensuring high availability, fault tolerance, and graceful degradation. • Build comprehensive monitoring, logging, and alerting using Prometheus, Grafana, and ELK stack. • Identify and resolve performance bottlenecks across infrastructure domains.