Senior GPU Engineer: Scale High-Performance Cloud GPUs

Vultr; United States; Remote; USD 190,000 - 210,000; Full time

Benefits offered by this job

100% company-paid insurance premiums

401(k) matching 100% up to 4%

Professional development reimbursement

Holidays + PTO + sabbatical

Remote office stipend

Internet reimbursement

Gym reimbursement

Wellable subscription

Job summary

Vultr is seeking a highly skilled and experienced Senior GPU Engineer to design, optimize, and scale GPU infrastructure for AI training and inference workloads. The ideal candidate has deep experience with GPU systems, distributed performance tuning, and leading engineering initiatives across cross-functional teams.

This role is highly visible in a fast-growing technology company and will require ownership of end-to-end system performance, from hardware qualification to cluster-level

Qualifications

• 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.

Responsibilities

• 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

Skills

GPU infrastructure

HPC

Distributed systems

Python

Linux

Automation frameworks

Performance debugging

Networking concepts

Tools

NCCL

Ansible

GPU hardware diagnostics

Job description

Vultr is seeking a highly skilled and experienced Senior GPU Engineer to design, optimize, and scale GPU infrastructure for AI training and inference workloads. The ideal candidate has deep experience with GPU systems, distributed performance tuning, and leading engineering initiatives across cross-functional teams.

This role is highly visible in a fast-growing technology company and will require ownership of end-to-end system performance, from hardware qualification to cluster-level