Senior Staff Deployment Automation Engineer

Crusoe is on a mission to accelerate the abundance of energy and intelligence. As the only vertically integrated AI infrastructure company built from the ground up, we own and operate each layer of the stack — from electrons to tokens — to power the world's most ambitious AI workloads. When you join Crusoe, you join a team that is building the future, faster.

We're in the midst of the greatest industrial revolution of our time. The demand for AI compute is boundless, and power is a bottleneck. We're solving that — with an energy-first approach that makes AI infrastructure better for the world and faster for the people innovating with AI.

We're looking for problem-solving, opportunity-finding teammates with a sense of urgency, who believe in the scale of our ambition and thrive on a path not fully paved — people who want to grow their careers alongside a team of experts across energy, manufacturing, data center construction, and cloud services.

If you want to do the most meaningful work of your career, help our customers and partners advance their AI strategies, and be part of a high-performing team that believes in each other, come build with us at Crusoe.

As a Senior Staff/Principal Deployment Automation Engineer for the Compute Team, you will be responsible for deployment and testing automation of large-scale, multi-node GPU clusters. You will own the CI/CD infrastructure, including both deployment and integration testing, for a rapidly scaling fleet of virtualized GPU and CPU hosts across our AI Cloud. Your role is critical in ensuring the stability of the low-level infrastructure and enabling teams across our Cloud Infrastructure organization to quickly and reliably release, test, and deploy their artifacts across our datacenters.

San Francisco, Sunnyvale, Bellevue (Onsite)

• Deployment and Integration Testing Ownership: Completely own deployment and integration testing automation for all bare-metal, on-premise systems across Crusoe’s AI Cloud Stack.

• CI/CD Automation and Tooling: Build CI/CD platforms that enable developers to quickly test, iterate, and deploy critical, low-level systems and applications.

• Multi-Node Scaling Validation: Design and execute large-scale validation tests across multi-node virtualized clusters to ensure linear scaling and stability of GPU workloads.

• Configuration Management and Observability: Maintain and scale bare-metal Linux configurations using a mix of custom and off the shelf tooling such as Gitlab, Ansible, AWX, osquery, etc.

• Deployment Orchestration: Create control applications to coordinate canary deployments on live production systems, run Blue/Green testing, and perform automatic rollback where necessary.

• Cluster Orchestration: Develop and maintain automation frameworks in Python or Go to dynamically provision, configure, and stress-test multi-node virtualized environments.

• Create automated test suites leveraging tools like fio, stress-ng, and iperf to ensure performance and multi-tenant isolation of CPU and GPU hosts.

• Education & Experience: 12+ 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.

• Experience building and deploying automated integration testing for an AI Cloud Environment, ranging from low-level Linux Systems up to Distributed Control Planes.

• Working knowledge of the modern infrastructure stack, including Kubernetes, Docker, Terraform, and Postgres.

• CI/CD & Gitlab: Intimate knowledge of CI/CD pipelines and Gitlab Tooling to enable stable infrastructure releases across multiple datacenters.

• Configuration Management: Previous experience with at least 1-2 configuration management systems, including Ansible, Puppet, Chef, or SaltStack.

• Automation & Scripting: Advanced proficiency in Python and/or Bash for automating complex cluster-wide test scenarios.

• System Internals: Knowledge of Linux kernel internals, specifically PCIe topology, VFIO, and memory management (HugePages, IOMMU).

• Distributed GPU Ecosystems: Familiarity with NVIDIA (CUDA/NCCL) and/or AMD (ROCm/RCCL) stacks in a multi-node context.

• Networking Knowledge: Strong 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).