Site Reliability Engineer (SRE) - REMOTE
Systems Engineer / Site Reliability Engineer (SRE) - REMOTE Job Type: Contract Industry: AI Infrastructure / Cloud Computing Experience Level: Senior / Lead Specialization: GPU Infrastructure, Linux Systems Engineering, Site Reliability Engineering Job Summary We are seeking an experienced Systems Engineer / Site Reliability Engineer (SRE) to design, automate, and operate the core infrastructure platform supporting high-density AI inference environments. In this role, you will work at the intersection of bare-metal accelerator hardware, Linux systems engineering, Kubernetes orchestration, and large-scale distributed infrastructure. You will be responsible for the reliability, scalability, performance optimization, and lifecycle automation of compute infrastructure supporting large-scale GPU workloads. The ideal candidate will have extensive experience in Linux systems engineering, hyperscale infrastructure, GPU computing environments, and production-grade automation. You should have hands-on expertise with NVIDIA drivers, CUDA, Kubernetes, infrastructure as code, and automated provisioning technologies.
Key Responsibilities
Design, build, and maintain automated infrastructure provisioning pipelines using PXE, iPXE, and Zero Touch Provisioning (ZTP). Manage and optimize Kubernetes clusters supporting large-scale GPU and AI inference workloads. Perform advanced Linux systems administration, kernel tuning, and performance optimization. Troubleshoot Linux kernel, systemd, NUMA, PCIe, and storage I/O performance issues. Manage GPU infrastructure, including NVIDIA drivers, CUDA, NCCL, and fabric managers. Develop infrastructure automation using Python or Go. Implement configuration management and infrastructure as code using Terraform, Ansible, and SaltStack. Establish and maintain SLIs, SLOs, monitoring, and observability frameworks. Build automated self-healing mechanisms to improve infrastructure reliability and availability. Troubleshoot distributed systems, hardware, networking, and GPU performance bottlenecks. Drive root cause analysis (RCA) and implement permanent solutions to recurring infrastructure issues. Improve operational efficiency through automation and reduction of manual intervention. Support the lifecycle management of large-scale compute and accelerator infrastructure.
Required Qualifications
Strong experience as a Systems Engineer, Site Reliability Engineer, or Infrastructure Engineer. Extensive hands-on experience with Linux internals, including kernel tuning, systemd, and performance troubleshooting. Strong experience managing Kubernetes clusters in production environments. Experience with bare-metal provisioning technologies such as PXE, iPXE, and ZTP. Hands-on experience with NVIDIA GPU infrastructure, drivers, CUDA, and NCCL. Strong understanding of NUMA architectures, PCIe subsystems, and storage I/O optimization. Proficiency in Python or Go for infrastructure automation and tooling. Experience with Terraform, Ansible, or SaltStack. Knowledge of monitoring, telemetry, SLIs, SLOs, and infrastructure observability. Experience supporting large-scale distributed systems and hyperscale infrastructure. Strong troubleshooting, debugging, and root cause analysis skills.
Preferred Qualifications
Experience working with GPU cloud providers, AI infrastructure companies, or hyperscale cloud environments. Previous experience at companies such as CoreWeave, Nebius, Crusoe Cloud, AWS, or Google Cloud. Experience with high-performance computing (HPC) and distributed GPU clusters. Familiarity with Prometheus, Grafana, VictoriaMetrics, and OpenTelemetry. Understanding of high-performance networking technologies, including RoCEv2 and InfiniBand. Experience with automated infrastructure recovery and self-healing systems. Experience operating infrastructure across thousands of bare-metal servers or accelerator nodes. Ideal Candidate Profile We are looking for a hands-on infrastructure engineer with deep Linux systems expertise and strong SRE fundamentals. The ideal candidate should understand how bare-metal hardware, GPU accelerators, operating systems, and Kubernetes work together to support large-scale AI inference workloads. Candidates with experience operating GPU clusters, hyperscale compute infrastructure, and highly automated data center environments will be strongly preferred.