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Salary: USD 170,000 - 205,000 / annual
Crusoe is a vertically integrated AI infrastructure company building the complete stack from energy to tokens to power large-scale AI workloads. The company is solving the power bottleneck in AI compute through an energy-first approach.
As a Senior Deployment Automation Engineer for the Compute Team, you will own deployment and testing automation for large-scale, multi-node GPU clusters. You'll develop CI/CD infrastructure for a rapidly scaling fleet of virtualized GPU and CPU hosts across Crusoe's AI Cloud, ensuring stability of low-level infrastructure and enabling teams across the Cloud Infrastructure organization to quickly and reliably release, test, and deploy artifacts across datacenters.
Key responsibilities include:
- Developing applications and systems for deployment and integration testing on bare-metal, on-premise systems across the AI Cloud Stack
- Building CI/CD platforms that enable developers to quickly test, iterate, and deploy critical, low-level systems and applications
- Designing and executing large-scale validation tests across multi-node virtualized clusters to ensure linear scaling and stability of GPU workloads
- Maintaining and scaling bare-metal Linux configurations using tools such as Gitlab, Ansible, osquery, and custom tooling
- Creating control applications to coordinate canary deployments on live production systems, run Blue/Green testing, and perform automatic rollback
- Developing and maintaining automation frameworks in Python or Go to dynamically provision, configure, and stress-test multi-node virtualized environments
- Creating automated test suites leveraging tools like fio, stress-ng, and iperf to ensure performance and multi-tenant isolation of CPU and GPU hosts
Requirements:
- 5+ years of experience with demonstrated ability to competently and independently perform responsibilities
- Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related technical field
- Experience building and deploying automated integration testing, ranging from low-level Linux systems up to distributed control planes
- Proven track record of designing and deploying microservice applications end-to-end in a distributed cloud environment
- Strong understanding of how cloud resources (compute, network, storage) are abstracted and managed in an IaaS environment
- Working knowledge of modern infrastructure stack including Kubernetes, Docker, Terraform, and Postgres
- Intimate knowledge of CI/CD pipelines and Gitlab tooling to enable stable infrastructure releases across multiple datacenters
- Previous experience with at least 1-2 configuration management systems (Ansible, Puppet, Chef, or SaltStack)
- Knowledge of Linux kernel internals, specifically PCIe topology, VFIO, and memory management (HugePages, IOMMU)
Bonus qualifications:
- Familiarity with NVIDIA (CUDA/NCCL) and/or AMD (ROCm/RCCL) stacks in multi-node context
- 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 (NVIDIA Nsight, AMD Omniperf)
- Knowledge of containerized orchestration for GPUs (Kubernetes with specialized device plugins)
