Vultr

1 week ago

GPU Performance and Benchmarking Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Drive GPU performance validation and optimization for AI training and inference workloads through rigorous benchmarking and systematic tuning. Profile and characterize GPU workloads, identify bottlenecks, establish performance baselines, and develop repeatable benchmarking automation. Analyze results and deliver actionable recommendations to engineering teams while validating new GPU hardware platforms and tracking emerging GPU architectures and software releases.

Location: United States

Workplace: Remote

Employment Type: Full time

Job Function: DevOps, Cloud & Infrastructure

Seniority: Mid level

Key Responsibilities

•Design and execute performance benchmarks for AI training and inference workloads.
•Profile and characterize GPU workloads to identify bottlenecks and optimization opportunities.
•Tune workload parameters (batch size, precision, parallelism, memory) to maximize throughput.
•Develop benchmarking tools, scripts, and automation for repeatable performance validation.
•Validate performance of new GPU hardware platforms and report results with actionable recommendations.

Pay and Benefits

Salary: USD 140,000 - 150,000

Perks:Health InsuranceDentalVision401kLearning BudgetPaid LeaveRemote WorkGym MembershipWellness Stipend

Key Requirements

•3–7 years of experience in GPU performance engineering, benchmarking, or HPC.
•Hands-on experience with GPU profiling and benchmarking tools such as NVIDIA Nsight, DCGM, nccl-tests, MLPerf, or GPU-Burn.
•Strong understanding of AI/ML workload performance characteristics for training vs. inference, including batch sizing and precision modes.
•Experience tuning performance parameters across GPU, CUDA, and framework layers.
•Proficiency in Python for benchmark scripting and data analysis, plus basic Linux and server hardware knowledge.

Experience:3-7 yearsHPCAI/ML

Skills:Analytical skillsWritten communicationDocumentationCollaboration

Tech Stack:NVIDIA NsightDCGMNccl-testsMLPerfGPU-BurnPythonCUDALinuxInfiniBandRoCENCCL

Company Brief

Vultr

Provides cloud infrastructure services including VPS, dedicated instances, block storage, and bare metal across global data centers. Targets developers and businesses with simple, high-performance, and cost-effective cloud compute and networking solutions.

Industry: Cloud Computing

Company Size: Large (251 to 1,000 employees)

Growth: Established Company

Headquarters: West Palm Beach, United States

Founded: 2014

Website LinkedIn

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Vultr

1 week ago

GPU Performance and Benchmarking Engineer

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 2 hours agoStatus: Live

Job Summary

Location: United States

Workplace: Remote

Employment Type: Full time

Job Function: DevOps, Cloud & Infrastructure

Seniority: Mid level

Key Responsibilities

•Design and execute performance benchmarks for AI training and inference workloads.
•Profile and characterize GPU workloads to identify bottlenecks and optimization opportunities.
•Tune workload parameters (batch size, precision, parallelism, memory) to maximize throughput.
•Develop benchmarking tools, scripts, and automation for repeatable performance validation.
•Validate performance of new GPU hardware platforms and report results with actionable recommendations.

Pay and Benefits

Salary: USD 140,000 - 150,000

Perks:Health InsuranceDentalVision401kLearning BudgetPaid LeaveRemote WorkGym MembershipWellness Stipend

Key Requirements

•3–7 years of experience in GPU performance engineering, benchmarking, or HPC.
•Hands-on experience with GPU profiling and benchmarking tools such as NVIDIA Nsight, DCGM, nccl-tests, MLPerf, or GPU-Burn.
•Strong understanding of AI/ML workload performance characteristics for training vs. inference, including batch sizing and precision modes.
•Experience tuning performance parameters across GPU, CUDA, and framework layers.
•Proficiency in Python for benchmark scripting and data analysis, plus basic Linux and server hardware knowledge.

Experience:3-7 yearsHPCAI/ML

Skills:Analytical skillsWritten communicationDocumentationCollaboration

Tech Stack:NVIDIA NsightDCGMNccl-testsMLPerfGPU-BurnPythonCUDALinuxInfiniBandRoCENCCL

Company Brief

Vultr

Industry: Cloud Computing

Company Size: Large (251 to 1,000 employees)

Growth: Established Company

Headquarters: West Palm Beach, United States

Founded: 2014

Website LinkedIn