GPU Performance and Benchmarking Engineer

🕒 July 13

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Vultr

WebsiteLinkedInAll Job Openings

201 - 500 employees

Founded 2014

🤖 Artificial Intelligence

🤝 B2B

🔧 Hardware

💰 $329M Debt Financing - Vultr on 2025-06

Artificial Intelligence • B2B • Hardware

Vultr is a global cloud infrastructure provider offering on-demand virtual machines, bare-metal servers, GPU-accelerated instances, managed databases, object and block storage, Kubernetes, and networking services. The platform emphasizes AI and HPC workloads with a broad selection of AMD and NVIDIA GPUs, fast networking, and 32+ data center regions, plus a marketplace of deployable apps and developer-friendly APIs. Vultr targets developers and businesses seeking affordable, scalable, and compliant cloud compute and storage alternatives to hyperscalers.

📋 Description

• Design and execute performance benchmarks for AI training and inference workloads • Profile and characterize GPU workloads to identify performance bottlenecks and optimization opportunities • Systematically tune workload parameters (batch size, precision, parallelism, memory, etc.) to maximize throughput • Establish and maintain performance baselines and success criteria across GPU platforms • Develop benchmarking tools, scripts, and automation for repeatable performance validation • Analyze and report benchmark results with actionable recommendations for engineering teams • Validate performance of new GPU hardware platforms before production deployment • Collaborate with GPU Engineers and Fabric Engineers to correlate performance with system-level health • Track and evaluate emerging GPU architectures and software releases for performance impact • Document benchmarking methodologies, tuning playbooks, and performance best practices

🎯 Requirements

• 3–7 years of experience in GPU performance engineering, benchmarking, or HPC • Hands-on experience with GPU profiling and benchmarking tools (e.g., NVIDIA Nsight, DCGM, nccl-tests, MLPerf, GPU-Burn) • Strong understanding of AI/ML workload performance characteristics (training vs. inference, batch sizing, precision modes) • Experience tuning performance parameters across GPU, CUDA, and framework layers • Familiarity with major GPU platforms (NVIDIA, AMD) and their performance tooling • Proficiency in Python for benchmark scripting and data analysis • Basic understanding of Linux systems and server hardware • Familiarity with high-speed networking concepts (InfiniBand, RoCE, NCCL) and their impact on distributed performance • Strong analytical skills with the ability to translate data into clear recommendations • Excellent written communication skills for performance reports and documentation

🏖️ Benefits

• 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 • Commitment matters to Vultr! 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