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Rdma Gpu Jobs (NOW HIRING)

Genmo

GPU Performance Engineer

San Francisco, CA · On-site

RDMA/InfiniBand optimization experience * Contributions to GPU libraries or frameworks * Low-level debugging skills (PTX/SASS reading) Genmo is an Equal Opportunity Employer. Candidates are evaluated ...

Vultr

Strategic Technical Account Manager GPU

$115K - $140K/yr

Advise on cluster design: multi-GPU topology, NVLink/NVSwitch considerations, RDMA, Infiniband and RoCE Ethernet, networking throughput, and storage IOPS requirements. * Guide customers in selecting ...

Oracle

Principal Engineer - AI Networking

Seattle, WA · On-site

Knowledge of GPUDirect RDMA and GPU-aware communication technologies. * Experience developing congestion management, traffic engineering, or network resiliency solutions. * Familiarity with large ...

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Rdma Gpu information

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$42.5K

$123.8K

$175K

How much do rdma gpu jobs pay per year?

As of Aug 15, 2026, the average yearly pay for rdma gpu in the United States is $123,786.00, according to ZipRecruiter salary data. Most workers in this role earn between $104,000.00 and $142,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an RDMA GPU engineer, and why are they important?

To thrive as an RDMA GPU Engineer, you need a solid background in computer science or engineering, with expertise in GPU architectures, networking protocols, and RDMA (Remote Direct Memory Access) technologies. Familiarity with CUDA, InfiniBand, RoCE, and relevant profiling or debugging tools is typically required. Strong problem-solving, teamwork, and communication skills help in collaborating effectively on complex, performance-critical systems. These skills are crucial for optimizing high-performance computing applications and ensuring efficient data transfer between GPUs and networked devices.

What is an RDMA GPU?

RDMA GPUs are graphics processing units that support Remote Direct Memory Access (RDMA) technology, enabling direct memory transfers between the GPU and remote devices or other GPUs across a network without involving the host CPU. This technology is commonly used in high-performance computing, AI, and data centers to reduce latency and increase data throughput. RDMA GPUs allow faster data exchange for distributed computing tasks, such as large-scale machine learning training, by bypassing traditional network bottlenecks.

How does an RDMA GPU engineer typically collaborate with software and hardware teams to optimize performance?

As an RDMA GPU engineer, you’ll regularly work alongside both software developers and hardware architects to ensure that high-speed data transfers between GPUs and other components are efficient and reliable. This collaboration often involves troubleshooting bottlenecks, tuning drivers, and optimizing memory access patterns. You may also participate in code reviews, joint debugging sessions, and performance benchmarking to align system-level improvements. Effective communication across multidisciplinary teams is essential to deliver best-in-class solutions for demanding workloads like machine learning or scientific computing.

What is the difference between Rdma Gpu vs Network Engineer?

AspectRdma GpuNetwork Engineer
Required CredentialsComputer science or related degree, certifications in GPU computing or high-performance networkingNetworking certifications (CCNA, CCNP), degree in computer science or related field
Work EnvironmentData centers, high-performance computing labs, research facilitiesCorporate offices, data centers, telecommunication environments
Industry UsageAI, machine learning, scientific computing, data analyticsIT infrastructure, network design, security, and maintenance

Rdma Gpu specialists focus on optimizing GPU performance and high-speed data transfer using RDMA technology, primarily in computing and research environments. Network Engineers design, implement, and maintain network systems. While both roles involve high-tech infrastructure, Rdma Gpu roles are more specialized in GPU and high-performance data transfer, whereas Network Engineers focus on network connectivity and security.

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Rdma Gpu jobs near you
Infographic showing various Rdma Gpu job openings in the United States as of August 2026, with employment types broken down into 95% Full Time, 1% Part Time, 1% Temporary, and 3% Contract. Highlights an 81% Physical, 7% Hybrid, and 12% Remote job distribution, with an average salary of $123,786 per year, or $59.5 per hour.

GPU Performance Engineer

Genmo

San Francisco, CA • On-site

Full-time

Re-posted 11 days ago

Job description

We are Genmo, a research lab dedicated to building open, state-of-the-art models for video generation towards unlocking the right brain of AGI. Join us in shaping the future of AI and pushing the boundaries of what's possible in video generation.

We're seeking a GPU Performance Engineer to squeeze every last FLOP from our H100 infrastructure and optimize our model serving stack to its absolute limits.
The Role
You'll be our performance optimization expert, using advanced profiling tools to identify bottlenecks and implementing solutions that achieve 5-10x speedups. From writing custom CUDA kernels to eliminating cold start latency, you'll ensure our infrastructure delivers world-class performance. This role is perfect for someone who gets excited about microsecond optimizations and pushing hardware to its theoretical limits.
Key Responsibilities

  • Profile and optimize GPU workloads using Nsight Systems, nvprof, and custom instrumentation

  • Write high-performance CUDA and Triton kernels for critical model operations

  • Optimize cold start latency from seconds to milliseconds for our serving infrastructure

  • Tune memory access patterns, kernel fusion, and GPU utilization

  • Collaborate with ML engineers to optimize model implementations

  • Debug performance issues across the full stack from application to hardware

  • Implement custom memory pooling and allocation strategies

  • Share optimization techniques and build performance culture across teams

Qualifications

  • Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field

  • 5+ years systems programming experience with 3+ years focused on GPU optimization

  • Expert proficiency with GPU profiling tools (Nsight Systems, nvprof)

  • Strong CUDA programming skills with production kernel development

  • Deep understanding of GPU architecture (memory hierarchy, SMs, warps)

  • Track record of achieving significant performance improvements (5-10x)

  • Experience with Python and C++ in production environments

We Value

  • Experience with Triton kernel development

  • Knowledge of CUTLASS or similar high-performance libraries

  • Background in ML-specific optimizations (attention, transformers)

  • RDMA/InfiniBand optimization experience

  • Contributions to GPU libraries or frameworks

  • Low-level debugging skills (PTX/SASS reading)

Genmo is an Equal Opportunity Employer. Candidates are evaluated without regard to age, race, color, religion, sex, disability, national origin, sexual orientation, veteran status, or any other characteristic protected by federal or state law. Genmo, Inc. is an E-Verify company and you may review the Notice of E-Verify Participation and the Right to Work posters in English and Spanish.