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Rdma Gpu Jobs (NOW HIRING)

Nvidia

$135K - $181K/yr

Design and evolve a unified memory layer that spans GPU memory, pinned host memory, RDMA-accessible memory, SSD tiers, and remote file/object/cloud storage to support large-scale LLM inference.

CoreWeave

Staff Software Engineer, Storage

Bellevue, WA · On-site

$207K - $303K/yr

Work with technologies such as RDMA, GPU Direct Storage, RoCE, InfiniBand, SPDK, and distributed filesystems to optimize performance. * Improve reliability, durability, and observability across the ...

Cisco

Engineering Product Manager

Milpitas, CA · On-site

$191K - $281K/yr

Expertise in RDMA over Converged Ethernet (RoCE v2), GPUDirect RDMA, GPU cluster interconnect design, and/or lossless Ethernet fabric design. * Direct experience with AI infrastructure platforms and ...

Mirantis

Technical Product Manager, AI Cloud Networking

$170K - $197K/yr

Exposure to GPU or HPC cluster networking, including rail-optimized fabric design, GPUDirect RDMA, or collective communication libraries (NCCL, RCCL) * Experience with DPU/SmartNIC products, OVS/OVN ...

CoreWeave

Sr. Engineer, Storage

Livingston, NJ · On-site

$143K - $210K/yr

Work with technologies such as RDMA, GPU Direct Storage, and distributed filesystems protocols such as NFS or FUSE to optimize storage performance and efficiency. * Lead efforts to improve the ...

Mirantis

Technical Product Manager, AI Cloud Networking

$170K - $197K/yr

Exposure to GPU or HPC cluster networking, including rail-optimized fabric design, GPUDirect RDMA, or collective communication libraries (NCCL, RCCL) * Experience with DPU/SmartNIC products, OVS/OVN ...

CoreWeave

Staff Software Engineer, Storage

Sunnyvale, CA · On-site

$207K - $303K/yr

Work with technologies such as RDMA, GPU Direct Storage, RoCE, InfiniBand, SPDK, and distributed filesystems to optimize performance. * Improve reliability, durability, and observability across the ...

Cisco

Engineering Product Manager

Milpitas, CA · On-site

$191K - $281K/yr

Expertise in RDMA over Converged Ethernet (RoCE v2), GPUDirect RDMA, GPU cluster interconnect design, and/or lossless Ethernet fabric design. * Direct experience with AI infrastructure platforms and ...

CoreWeave

Sr. Engineer, Storage

Sunnyvale, CA · On-site

$143K - $210K/yr

Work with technologies such as RDMA, GPU Direct Storage, and distributed filesystems protocols such as NFS or FUSE to optimize storage performance and efficiency. * Lead efforts to improve the ...

CoreWeave

Staff Software Engineer, Storage

New York, NY · On-site

$207K - $303K/yr

Work with technologies such as RDMA, GPU Direct Storage, RoCE, InfiniBand, SPDK, and distributed filesystems to optimize performance. * Improve reliability, durability, and observability across the ...

SF Compute

HPC/ GPU Cluster Architect

San Francisco, CA · On-site

$220K - $300K/yr

When people finance GPU clusters, the datacenters housing them, and the infrastructure powering ... Strong Linux systems administration experience, including kernel drivers, RDMA stack tuning, and ...

CoreWeave

Sr. Engineer, Storage

New York, NY · On-site

$143K - $210K/yr

Work with technologies such as RDMA, GPU Direct Storage, and distributed filesystems protocols such as NFS or FUSE to optimize storage performance and efficiency. * Lead efforts to improve the ...

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All JobsRdma Gpu Jobs

Rdma Gpu information

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$42.5K

$123.8K

$175K

How much do rdma gpu jobs pay per year?

As of Aug 6, 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.

More about Rdma Gpu jobs
What cities are hiring for Rdma Gpu jobs? Cities with the most Rdma Gpu job openings:
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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, and 5% Contract. Highlights an 53% In-person, 5% Hybrid, and 42% Remote job distribution, with an average salary of $123,786 per year, or $59.5 per hour.

Principal Software Engineer - Large-Scale LLM Memory and Storage Systems

Nvidia

On-site, Remote

$135K - $181K/yr

Full-time

Re-posted 29 days ago

Nvidia rating

9.6

Company rating: 9.6 out of 10

Based on 17 frontline employees who took The Breakroom Quiz

8th of 242 rated software companies

Job description

NVIDIA Dynamo is a high-throughput, low-latency inference framework for serving generative AI and reasoning models across multi-node distributed environments. Built in Rust for performance and Python for extensibility, Dynamo orchestrates GPU shards, routes requests, and manages shared KV cache across heterogeneous clusters so that many accelerators feel like a single system at datacenter scale. As large language models rapidly outgrow the memory and compute budget of any single GPU, this platform enables efficient, resilient deployment of cutting-edge LLM workloads.


We are seeking a Principal Systems Engineer to define the vision and roadmap for memory management of large-scale LLM and storage systems.


What you'll be doing:

  • Design and evolve a unified memory layer that spans GPU memory, pinned host memory, RDMA-accessible memory, SSD tiers, and remote file/object/cloud storage to support large-scale LLM inference.

  • Architect and implement deep integrations with leading LLM serving engines (such as vLLM, SGLang, TensorRT-LLM), with a focus on KV-cache offload, reuse, and remote sharing across heterogeneous and disaggregated clusters.

  • Co-design interfaces and protocols that enable disaggregated prefill, peer-to-peer KV-cache sharing, and multi-tier KV-cache storage (GPU, CPU, local disk, and remote memory) for high-throughput, low-latency inference.

  • Partner closely with GPU architecture, networking, and platform teams to exploit GPUDirect, RDMA, NVLink, and similar technologies for low-latency KV-cache access and sharing across heterogeneous accelerators and memory pools.

  • Mentor senior and junior engineers, set technical direction for memory and storage subsystems, and represent the team in internal reviews and external forums (open source, conferences, and customer-facing technical deep dives).

What we need to see:

  • Masters or PhD or equivalent experience

  • 15+ years of experience building large-scale distributed systems, high-performance storage, or ML systems infrastructure in C/C++ and Python, with a track record of delivering production services.

  • Deep understanding of memory hierarchies (GPU HBM, host DRAM, SSD, and remote/object storage) and experience designing systems that span multiple tiers for performance and cost efficiency.

  • Distributed caching or key-value systems, especially designs optimized for low latency and high concurrency.

  • Hands-on experience with networked I/O and RDMA/NVMe-oF/NVLink-style technologies, and familiarity with concepts like disaggregated and aggregated deployments for AI clusters.

  • Strong skills in profiling and optimizing systems across CPU, GPU, memory, and network, using metrics to drive architectural decisions and validate improvements in TTFT and throughput.

  • Excellent communication skills and prior experience leading cross-functional efforts with research, product, and customer teams.

Ways to stand out from the crowd:

  • Prior contributions to open-source LLM serving or systems projects focused on KV-cache optimization, compression, streaming, or reuse.

  • Experience designing unified memory or storage layers that expose a single logical KV or object model across GPU, host, SSD, and cloud tiers, especially in enterprise or hyperscale environments.

  • Publications or patents in areas such as LLM systems, memory-disaggregated architectures, RDMA/NVLink-based data planes, or KV-cache/CDN-like systems for ML.

With highly competitive salaries and a comprehensive benefits package, NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, due to outstanding growth, our special engineering teams are growing fast. If you're a creative and autonomous engineer with a genuine passion for technology, we want to hear from you!

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 272,000 USD - 431,250 USD.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until January 13, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering a diverse work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

What Nvidia employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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