Senior Automation Engineer (Compute)

Join Crusoe Energy, a leading company in the AI Cloud industry. As a Senior Automation Engineer, you will be responsible for deployment and testing automation of large-scale GPU clusters, developing CI/CD infrastructure, and ensuring the stability of the low-level infrastructure. You will work with cutting-edge technology and have a significant impact on the company's success.

• As a Senior Deployment Automation Engineer for the Compute Team, you will be responsible for the deployment and testing automation of large-scale, multi-node GPU clusters, developing the CI/CD infrastructure for a rapidly scaling fleet of virtualized GPU and CPU hosts, and ensuring the stability of the low-level infrastructure to enable teams across the organization to quickly and reliably release, test, and deploy their artifacts.

• CI/CD & Gitlab: Intimate knowledge of CI/CD pipelines and Gitlab Tooling to enable stable infrastructure releases across multiple datacenters
• Working knowledge of the modern infrastructure stack, including Kubernetes, Docker, Terraform, and Postgres
• Configuration Management: Previous experience with at least 1-2 configuration management systems, including Ansible, Puppet, Chef, or SaltStack
• Education & Experience: 5+ YOE demonstrated ability to competently and independently perform responsibilities plus Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or a related technical field
• Proven track record of designing and deploying microservice applications end to end in a distributed Cloud Environment
• System Internals: Knowledge of Linux kernel internals, specifically PCIe topology, VFIO, and memory management (HugePages, IOMMU)
• A strong understanding of how cloud resources (Compute, Network, Storage) are abstracted and managed in an IaaS environment
• Experience building and deploying automated integration testing, ranging from low-level Linux Systems up to Distributed Control Planes
• Distributed GPU Ecosystems: Familiarity with NVIDIA (CUDA/NCCL) and/or AMD (ROCm/RCCL) stacks in a multi-node context
• Networking Knowledge: Understanding of RDMA, RoCE, and InfiniBand protocols and their implementation in virtualized systems
• Experience with MNNVL (Multi-Node NVLink) or specialized AI fabric architectures
• Familiarity with hardware-level debugging tools and performance profilers (e.g., NVIDIA Nsight, AMD Omniperf)
• Knowledge of containerized orchestration for GPUs (e.g., Kubernetes with specialized device plugins)