About the job
As a Staff Cloud Support Engineer, you are a technical authority within Crusoe Cloud and a force multiplier across Customer Experience, SRE, Networking, Fleet, and Product teams. You operate beyond ticket resolution. You design reliability guardrails, influence architecture decisions, mentor engineers, and directly protect revenue by preventing large-scale incidents. You bring deep expertise in Linux systems, Kubernetes, networking, and AI/ML infrastructure, and apply that knowledge with strong customer focus. You are comfortable operating in ambiguity, leading incident response, and shaping how Crusoe scales high-performance AI infrastructure globally.
Responsibilities
- Serve as highest-level escalation point for complex P1/P0 incidents.
- Lead cross-functional root cause investigations involving compute, networking (IB/RDMA/RoCE), storage, and orchestration layers.
- Partner with SRE, Software teams (Storage, Networking, Compute, K8) to design systemic fixes rather than recurring workarounds.
- Design and improve node validation, burn-in processes, performance baselining, and release readiness.
- Influence Kubernetes architecture, workload orchestration (Slurm, Terraform), and AI/ML cluster stability.
- Reduce MTTR and incident recurrence through structural improvements.
- Troubleshoot NCCL, IB, GPU driver/firmware issues, distributed training failures.
- Support complex AI workloads (training + inference) with performance tuning and observability improvements.
- Act as technical advisor during high-risk customer incidents.
- Deliver executive-ready RCAs with clarity and confidence.
- Drive trust through transparency and technical depth.
- Mentor P3/P4 engineers.
- Define SOPs and technical standards for support excellence.
- Partner with Enablement to raise the technical bar across the organization.
Qualifications
Minimum
- 8+ years experience in SRE, DevOps, HPC, or Cloud Infrastructure roles.
- Advanced Linux systems expertise.
- Deep Kubernetes operational experience (CKA-level or higher).
- Strong networking knowledge: Infiniband, RDMA, RoCE, SDN.
- Experience supporting AI/ML workloads at scale (GPU clusters).
- Proven track record of resolving multi-layer, distributed system failures.
- Strong customer communication and executive-facing presence.
Preferred
No preferred qualifications listed.