Hiring: Sr. AI Infrastructure Engineer Company: ECLARO Location: Costa Mesa, CA Job Posted Time: 2026-09-15 20:01:44 Target Skills & Keywords : HBase, Infrastructure as Code, Kubernetes, Linear About the job Experience: •10+ years in a hands on infrastructure, HPC, or datacenter engineering role supporting GPU compute at scale. Required Skills: •Rack, stack, cable, and bring up GPU compute (H200/B200/B300, NVL72) including physical topology, power, cooling, firmware/BIOS, and burn in validation. •Build and tune the interconnect fabric (NVLink, InfiniBand, RoCE, Spectrum-X) connecting hundreds of GPUs into low latency training and inference clusters. •Integrate high performance parallel storage (VAST, DDN, Weka) to sustain the throughput demanded by distributed training and terabyte scale multi modal datasets across Company's programs. •Automate cluster deployment and configuration end to end, including infrastructure as code for bring up, firmware/driver management, and fabric config, so new capacity comes online with minimal manual work. •Operate and extend our Kubernetes/Run:AI environment for GPU scheduling, quota management, and multi tenant workload isolation across research and engineering teams company wide. •Own fleet health: monitoring, alerting, and rapid triage of hardware and network faults (bad transceivers, GPU Xid errors, NCCL/collective failures, RoCE congestion). •Onboard engineers and researchers onto the platform and act as their escalation point, working directly alongside them to debug, train, and optimize their workloads whenever infrastructure, not the model, is the bottleneck. •Partner with product facing teams across Company to understand emerging compute needs and translate them into platform capability. Qualifications: •10+ years in a hands on infrastructure, HPC, or datacenter engineering role supporting GPU compute at scale. •Hands on experience with H200/B200/B300 (or comparable) GPU systems: bring up, cabling, firmware/driver management. •Experience with high performance interconnects (NVLink, InfiniBand, RoCE, Spectrum-X) in clusters of hundreds of GPUs. •Experience with high performance parallel storage (VAST, DDN, Weka, Lustre, or similar). •Kubernetes required; Run:AI or similar GPU scheduling/orchestration experience strongly preferred. •Strong automation Compensation: •$166,000 •$220,000 Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!