Senior Systems Engineer (AI)

TL;DR

Senior Systems Engineer (AI): Leading hands-on bringup of network clusters in data center environments with an accent on node, rack, and network deployment validation. Focus on tuning high-speed fabrics, debugging performance issues, and building repeatable, scalable infrastructure processes.

Location: Must be based in the US and comfortable working onsite in data center environments.

Company

hirify.global is a startup building next-generation AI infrastructure, focused on delivering performant and scalable network clusters for frontier AI workloads.

What you will do

• Execute end-to-end bringup of network nodes and racks from installation to production.
• Validate BIOS, BMC, firmware configurations, and overall network health.
• Bring up and validate high-speed network fabrics including InfiniBand, RoCE, and Ethernet.
• Configure leaf/spine connectivity and run cluster-wide burn-in and stress testing.
• Troubleshoot hardware, firmware, and fabric-level issues to optimize performance.
• Automate provisioning processes and improve deployment documentation.

Requirements

• 5–8+ years in infrastructure engineering, hardware deployment, or data center operations.
• Hands-on experience deploying network servers like HGX or DGX platforms.
• Deep understanding of high-speed networking fabrics (InfiniBand, RoCE, Ethernet).
• Strong Linux systems knowledge.
• Proven ability to troubleshoot distributed systems performance issues.
• Must be able to work onsite in data center environments as required.

Nice to have

• Experience in AI/ML infrastructure or HPC environments.
• Familiarity with NCCL, CUDA, and RDMA.
• Proficiency in automation tools like Python, Ansible, Terraform, or Bash.
• Experience managing high-density power and cooling environments.

Culture & Benefits

• Fast-paced startup environment with an emphasis on urgency and ownership.
• Opportunity to build core AI infrastructure from the ground up.
• Direct collaboration with networking, systems software, and data center teams.
• Focus on developing repeatable, high-scale systems and processes.