Senior GPU Engineer

Vultr

. Design, optimize, and scale GPU infrastructure for AI training and inference workloads .

Posted 10/6/2026full-timeRemote • United StatesSenior💰 $190,000 - $210,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and optimizing GPU infrastructure for AI workloads, with a strong focus on performance validation, automation frameworks, and troubleshooting complex distributed systems. Proven ability to mentor engineers and drive cross-functional initiatives to enhance system reliability and efficiency.

Highest-signal resume keywords

GPU Infrastructure ExpertiseLinux Systems ProficiencyPython Programming SkillsAutomation Frameworks ExperiencePerformance Validation Methodologies

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

GPU InfrastructureHigh-Performance Computing (HPC)Distributed SystemsPerformance DebuggingValidation Standards DefinitionCluster ProvisioningPerformance BaselinesAutomation FrameworksNCCL Communication LibrariesSystem Reliability Improvement

Soft Skills

Excellent CommunicationCross-Team CollaborationMentoring

Tools & Technologies

AnsibleLinux SystemsServer HardwareHigh-Speed Networking

Industry Keywords

GPU ClustersPerformance BottlenecksValidation FrameworksTest SuitesProactive TestingTuning

Tech Stack

Tools & technologies

AnsibleDistributed SystemsLinuxPython

About the role

Key responsibilities & impact

Design, optimize, and scale GPU infrastructure for AI training and inference workloads
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 such as 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

Requirements

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
Excellent communication and cross-team collaboration skills
Located in the United States
Legally authorized to work in the United States
Must answer whether employment visa sponsorship is required

Benefits

Comp & perks

100% company-paid insurance premiums for employee medical, dental and vision plans
401(k) plan that matches 100% up to 4%, with immediate vesting
Professional Development Reimbursement of $2,500 each year
11 Holidays + Paid Time Off Accrual + Rollover Plan
Increased PTO at 3 year and 10 year anniversary
1 month paid sabbatical every 5 years
Anniversary Bonus each year
$500 stipend for remote office setup in first year + $400 each following year
Internet reimbursement up to $75 per month
Gym membership reimbursement up to $50 per month
Company paid Wellable subscription