Senior Deployment Engineer (AI)

TL;DR

Senior Deployment Engineer (AI): Leading the hands-on bringup of high-performance GPU clusters in data center environments with an accent on hardware integration, high-speed fabric tuning, and performance validation. Focus on executing end-to-end node and rack deployments, troubleshooting complex distributed hardware issues, and building repeatable, scalable infrastructure processes.

Location: Must be based in the United States (Onsite travel required)

Company

A startup building next-generation AI infrastructure and scalable GPU clusters for frontier AI workloads.

What you will do

• Execute end-to-end bringup of GPU nodes and racks from installation to production readiness.
• Validate BIOS, BMC, firmware configurations, and overall GPU cluster health.
• Configure and validate high-speed network fabrics including InfiniBand and RoCE.
• Perform cluster-wide burn-in, stress testing, and performance validation using NCCL and RDMA.
• Develop automation playbooks to transform ad-hoc deployments into repeatable, scalable systems.
• Collaborate with networking and hardware vendors to troubleshoot and resolve deployment issues.

Requirements

• Must have 5–8+ years of experience in infrastructure engineering or data center operations.
• Hands-on experience deploying GPU servers such as HGX or DGX platforms.
• Proficiency with high-speed networking fabrics including InfiniBand, RoCE, and Ethernet.
• Strong Linux systems knowledge and troubleshooting skills for distributed performance issues.
• Must be comfortable working onsite in data center environments.
• Must be authorized to work in the United States.

Nice to have

• Experience in AI/ML infrastructure or HPC environments.
• Familiarity with CUDA, NCCL, and RDMA protocols.
• Automation proficiency using Python, Ansible, Terraform, or Bash.
• Experience managing high-density power and cooling data center environments.

Culture & Benefits

• Opportunity to work on foundational AI infrastructure at a fast-growing startup.
• High-impact role with significant ownership over infrastructure build-out.
• Focus on urgency, bias toward action, and engineering excellence.
• Direct collaboration with infrastructure and hardware teams.