Senior AI Infrastructure Engineer

About the role

Job Type: Direct Hire (no 3rd parties)

Job Title: Senior AI Infrastructure Engineer, Physical Infrastructure

Location: Costa Mesa, California, United States

Onsite: Yes, Hybrid

Must be authorized to work in USA and obtain a security clearance: No sponsorship

Compensation: $190-$220K + Benefits

ABOUT THE TEAM

We give engineers, researchers, and product teams across the company a place to deploy fast, scalable infrastructure without having to become infrastructure experts themselves. As Company's AI and autonomy ambitions grow, our team is responsible for delivering the next generation of compute, networking, and storage capabilities that make cutting edge model training and inference possible company wide.

ABOUT THE JOB

We’re looking for a Senior AI Infrastructure Engineer to lead the vision, execution, and long-term stability of how Company trains with GPUs at scale. In this role, you will take absolute ownership of cluster robustness, ensuring our high-performance GPU systems are highly available, fault-tolerant, and resilient for ML platform and research teams company-wide. This is a highly hands-on role where your primary focus is logical stability and automated resilience—building self-healing mechanisms to proactively detect and isolate hardware faults, tuning NCCL and high-speed networking, and optimizing Kubernetes, Run:AI, and Ray scheduling. By replacing manual triage with automated deployment tooling and deep observability, you will ensure our massive-scale training infrastructure runs seamlessly and scales without linear headcount growth.

WHAT YOU'LL DO

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.

REQUIRED 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 background. You build repeatable, automated deployment pipelines rather than manual processes.

Able to lift/move 50+ lbs and perform physical datacenter work (rack/stack/cable/troubleshoot).

Eligible to obtain and maintain an active U.S. Top Secret clearance.

PREFERRED QUALIFICATIONS

Experience with NVIDIA NVL72 rack scale systems.

Experience supporting LLM token serving/inference infrastructure alongside training clusters.

Network fabric tuning experience (congestion control, adaptive routing, QoS) for RoCE/InfiniBand at scale.

Familiarity with GPU/network observability tooling (DCGM, fabric telemetry) and automated fault detection.

Experience supporting infrastructure as a shared platform serving multiple internal customer teams with differing requirements.