Senior GPU Engineer

Overview

Design, optimize, and scale GPU infrastructure for AI training and inference workloads, owning end-to-end system performance from hardware qualification to cluster-level optimization.

What you'll do

• Own end-to-end validation of GPU clusters and new hardware platforms
• Lead hardware qualification and bring-up for new GPU platforms
• Analyze and resolve performance bottlenecks across GPU, CPU, PCIe, and network layers
• Establish and maintain validation frameworks, test suites, and performance baselines
• Develop and enhance automation frameworks for cluster provisioning and validation
• Define performance baselines and validation methodologies
• Troubleshoot complex distributed system issues, including communication libraries (e.g., NCCL)
• Improve system reliability through proactive testing and tuning
• Mentor engineers and elevate team capabilities
• Drive cross-functional initiatives to improve GPU cluster reliability and efficiency

What you'll need

• 5+ years of experience in GPU infrastructure, HPC, or distributed systems
• Strong expertise in Linux systems and server hardware
• Proven experience with large-scale GPU clusters
• Strong programming skills in Python (beyond basic scripting)
• Experience with automation frameworks (Ansible or similar)
• Experience defining validation standards and performance baselines for GPU infrastructure
• Strong debugging skills across system layers (hardware → OS → network)
• Familiarity with high-speed networking concepts to coordinate with fabric engineering teams
• Excellent communication and cross-team collaboration skills

Details

• Location: Remote, United States
• Compensation: $190,000 - $210,000; final compensation varies by experience, background/skill set, location, and applicable laws