Fleet Hardware Reliability Engineer for AI/ML Infra

Precision Labs; Southaven (MS); Hybrid; USD 90,000 - 140,000; Full time

Job summary

SpaceXSI seeks a hardware reliability engineer to diagnose fleet-wide failures, drive fixes with vendors, and improve RMA and spare strategies across data-center hardware. Candidates should have strong firmware analysis skills and experience with GPU, NIC, memory, and power systems.

The role requires collaboration with cross-functional teams, scripting for automation, and a data-driven approach to reliability in high-performance computing environments.

Qualifications

• Bachelor's degree in Systems Engineering, Electrical Engineering, Computer Science, or related field (or equivalent experience).
• 2+ years of hardware reliability engineering experience in high-performance computing or data centers.
• Proven firmware analysis, hardware specs review, and release validation experience.

Responsibilities

• Own named failure classes across GPU trays/baseboards, NIC/DPU, motherboard/PCIe switch, memory, power, thermal, cables/connectors, and rack-scale patterns.
• Run recurrence analysis and fleet-wide defect clustering; detect systemic patterns before they become fleet-scale loss.
• Build vendor escalation packages with evidence quality to drive action; collaborate with OEM/ODM manufacturers on firmware issues and manufacturing escapes.
• Feed findings into RMA policy, spare strategy, and stop-rules.
• Manage FA intake queue on rotation; keep queue age within SLA.

Skills

Python scripting

C/C++

Firmware analysis

RMA processes

Diagnostics tools

Data center hardware

Root cause analysis

Vendor negotiations

Education

Bachelor's degree in Systems Engineering / Electrical Engineering / CS

Tools

Logic analyzers

Diagnostic software

Job description

SpaceXSI seeks a hardware reliability engineer to diagnose fleet-wide failures, drive fixes with vendors, and improve RMA and spare strategies across data-center hardware. Candidates should have strong firmware analysis skills and experience with GPU, NIC, memory, and power systems.

The role requires collaboration with cross-functional teams, scripting for automation, and a data-driven approach to reliability in high-performance computing environments.