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Rdma Gpu Jobs (NOW HIRING)

Runpod, Inc.

Manager, HPC Storage Engineer

Evaluate and deploy cutting-edge capabilities such as NFS over RDMA, GPU Direct Storage (GDS) , and low-latency data paths for accelerated workloads. * Reliability & Scale: Establish best practices ...

Nvidia

Senior GPU System Architect

Santa Clara, CA · Hybrid

Collaborate with other teams to architect RDMA-capable hardware and define transport layer optimizations for GPU-based large scale AI workload deployments. * Use and modify system models, perform ...

CoreWeave

Senior Engineer, Storage Control Plane

Manhattan, NY · On-site

$115K - $158K/yr

... RDMA, GPU Direct Storage, RoCE, InfiniBand, SPDK, and distributed filesystems to optimize storage performance and efficiency. • Participate in efforts to improve the reliability, durability, and ...

CoreWeave

Senior Engineer, Storage Control Plane

Sunnyvale, CA · On-site

$122K - $167K/yr

... RDMA, GPU Direct Storage, RoCE, InfiniBand, SPDK, and distributed filesystems to optimize storage performance and efficiency. • Participate in efforts to improve the reliability, durability, and ...

CoreWeave

Senior Engineer, Storage Control Plane

Livingston, NJ · On-site

$113K - $156K/yr

... RDMA, GPU Direct Storage, RoCE, InfiniBand, SPDK, and distributed filesystems to optimize storage performance and efficiency. • Participate in efforts to improve the reliability, durability, and ...

CoreWeave

Senior Engineer, Storage Control Plane

Bellevue, WA · On-site

$117K - $161K/yr

... RDMA, GPU Direct Storage, RoCE, InfiniBand, SPDK, and distributed filesystems to optimize storage performance and efficiency. • Participate in efforts to improve the reliability, durability, and ...

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 ...

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 ...

Graphcore

GPU Architect

Milpitas, CA

Job Summary We are seeking a highly accomplished experienced GPU Architect to define the next ... Extensive hands-on experience characterizing data pathways across RDMA environments, and hardware ...

Graphcore

GPU Architect

Milpitas, CA · On-site

Job Summary We are seeking a highly accomplished experienced GPU Architect to define the next ... Extensive hands-on experience characterizing data pathways across RDMA environments, and hardware ...

CoreWeave

Principal Engineer, Storage

Bellevue, WA · On-site

$206K - $303K/yr

Work with technologies such as RDMA, GPU Direct Storage, RoCE, InfiniBand, SPDK, and distributed filesystems to optimize storage performance and efficiency. * Participate in efforts to improve the ...

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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 Jun 13, 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 are RDMA GPUs?

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 June 2026, with employment types broken down into 8% Internship, and 92% Full Time. Highlights an 84% In-person, 8% Hybrid, and 8% Remote job distribution, with an average salary of $123,786 per year, or $59.5 per hour.

Manager, HPC Storage Engineer

Runpod, Inc.

Remote

Full-time

Medical, Dental, Vision, PTO

Posted 15 days ago

Job description

Runpod is pioneering the future of AI and machine learning, offering cutting-edge cloud infrastructure for full-stack AI applications. Founded in 2022, we are a rapidly growing, well-funded, remote-first company with a global team across the US, Canada, and Europe. Our mission is to create a foundational platform that enables developers and companies to build, deploy, and scale custom AI systems with speed and flexibility.
As AI workloads continue to push the limits of throughput, latency, and parallelism, Runpod is investing heavily in next-generation storage architectures purpose-built for GPU-centric compute.
We are looking for an Engineering Manager, Datacenter Storage Engineering to lead the team responsible for Runpod's distributed storage infrastructure across all regions. This role owns the end-to-end storage stack - from NAND and NVMe devices through filesystems, transport protocols, and cluster-level deployment - ensuring performance, reliability, and scalability for AI workloads.
You will manage engineers designing and operating large-scale SAN and NFS-based systems, including high-performance shared filesystems for training workloads. This role requires deep technical fluency and architectural leadership, combined with strong people management and operational discipline.
Responsibilities
  • Own Distributed Storage Architecture: Define, evolve, and operate Runpod's global storage platforms, supporting training, inference, checkpointing, and dataset access at scale.
  • Build the Storage Engineering Team: Manage and grow a team of storage and systems engineers. Set clear ownership, technical direction, and operational standards across regions.
  • High-Performance Shared Filesystems: Design and operate large-scale SAN and NFS deployments, including performance-sensitive shared storage for GPU clusters.=
  • Advanced Filesystems & Platforms: Lead deployments and operations of VAST Data and experience with Lustre or similar parallel filesystems used in HPC and AI environments.
  • End-to-End Performance Ownership: Drive performance optimization from NAND and NVMe media through controllers, networking, and client access patterns.
  • Next-Generation Storage Technologies: Evaluate and deploy cutting-edge capabilities such as NFS over RDMA, GPU Direct Storage (GDS), and low-latency data paths for accelerated workloads.
  • Reliability & Scale: Establish best practices for replication, data tiering, data protection, failure recovery, capacity planning, and lifecycle management.
  • Automation & Observability: Build automation for provisioning, expansion, upgrades, and monitoring. Ensure deep observability into throughput, latency, and error characteristics.
  • Cross-Functional Collaboration: Partner with Datacenter Networking, GPU Platform, SRE, and Product teams to ensure storage systems meet evolving workload and customer needs.
  • Vendor & Partner Management: Own technical relationships with storage vendors, hardware partners, and colocation providers; drive roadmap alignment and issue resolution.
Requirements
  • Engineering Leadership Experience: 3+ years managing storage, systems, or infrastructure engineering teams in production environments.
  • Distributed Storage Expertise: 8+ years designing and operating large-scale storage systems, including SAN and NFS architectures at multi-petabyte scale.
  • VAST Data Experience: Hands-on experience deploying, operating, or deeply integrating VAST Data in production environments is required.
  • Parallel Filesystems: Experience with Lustre or comparable HPC filesystems (e.g., GPFS, BeeGFS) supporting high-concurrency workloads.
  • Low-Level Storage Knowledge: Deep understanding of NAND, NVMe, PCIe, storage controllers, and performance characteristics across the stack.
  • High-Performance Data Paths: Proven experience with NFS over RDMA, RDMA-capable transports, or similar technologies. Familiarity with GPU Direct Storage strongly preferred.
  • Linux Systems Expertise: Strong Linux internals knowledge, including filesystems, I/O scheduling, memory management, and tuning for performance workloads.
  • Operational Excellence: Experience running 24/7 storage platforms with strong incident response, change management, and post-mortem discipline.
  • Communication & Leadership: Ability to clearly communicate complex technical tradeoffs and lead teams through high-stakes infrastructure decisions.
  • Successful completion of a background check.
Preferred Qualifications
  • Experience supporting AI training pipelines, large-scale model checkpointing, and dataset streaming workloads.
  • Familiarity with RDMA fabrics and close collaboration with datacenter networking teams.
  • Experience designing storage systems for multi-tenant isolation and secure data access.
  • Background in hyperscale, HPC, or AI-focused infrastructure environments.
  • Experience building internal storage platforms or abstractions consumed by product teams.

What You'll Receive:
  • The competitive base pay for this position ranges from $150,000 - $240,000 USD. This salary range may be inclusive of several career levels at Runpod and will be narrowed during the interview process based on a number of factors, including the candidate's experience, qualifications, and location
  • Meaningful equity in a fast-growing company- everyone on the team receives stock options - your impact drives our growth, and you share in the upside.
  • Generous medical, dental & vision plans
  • Flexible PTO- take the time you need to recharge
  • Most roles are remote work first with an inclusive, collaborative teams utilizing slack as the main form of internal communication
  • Join a passionate team on the cutting edge of AI infrastructure - where culture, learning, and ownership are at the heart of how we scale.

Apply