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Rdma Gpu Jobs (NOW HIRING)

BitDeer

Senior GPU Systems & Fabric Engineer

Austin, TX · On-site +1

$180K - $320K/yr

Configure and optimize high-performance host networking stacks, including RDMA, SR-IOV, RoCEv2, and ... Implement and manage sophisticated GPU slicing technologies (MIG, vGPU) to enable efficient multi ...

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 ...

Data Direct Networks

Lab Administrator

Independence, KS · On-site

$70 - $110/hr

Experience with HPC/AI infrastructure (InfiniBand, RDMA, GPU clusters) * Experience with configuration management (Ansible, Puppet) We pride ourselves on our commitment to delivering tangible 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 ...

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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 Sep 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 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 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 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 1% As Needed, 97% Full Time, and 2% Contract. Highlights an 78% Physical, 6% Hybrid, and 16% Remote job distribution, with an average salary of $123,786 per year, or $59.5 per hour.

Senior GPU Systems & Fabric Engineer

BitDeer

Austin, TX • On-site, Remote

$180K - $320K/yr

Full-time

Posted 18 days ago

Key responsibilities

  • Architect and maintain integrations for GPU device plugins and Kubernetes Operators to expose hardware capabilities to the control plane.

  • Configure and optimize high-performance host networking stacks, including RDMA, SR-IOV, RoCEv2, and InfiniBand.

  • Build and manage automated hardware remediation pipelines using DCGM telemetry to identify, isolate, and reset degraded GPU/NIC components.

Job description

Bitdeer is a world-leading technology company for AI and Bitcoin mining infrastructure.
Bitdeer is committed to providing comprehensive Bitcoin mining solutions for its customers and building AI computational infrastructure to support the AI revolution. Bitdeer handles complex processes involved in computing such as equipment procurement, transport logistics, data center design and construction, equipment management, and daily operations. Bitdeer also offers advanced cloud capabilities to customers with high demand for artificial intelligence.
Headquartered in Singapore, Bitdeer has deployed data centers across multiple countries, including the United States, Norway, Bhutan, and Ethiopia.
To learn more, visit https://ir.bitdeer.com/
Position Overview
We are seeking a Senior GPU Systems & Fabric Engineer to serve as the critical bridge between our physical GPU/network infrastructure and the Kubernetes abstraction layer. You will be responsible for creating the high-performance 'hardware foundation' that makes AI-native cloud computing possible. This role requires deep expertise in Linux kernel internals, GPU architectures, and high-speed interconnects, as you will be tasked with transforming raw, bare-metal compute resources into scalable, resilient, and multi-tenant cloud primitives. You will drive the design of our fabric layer, ensuring that our AI workloads have the low-latency, high-bandwidth environment they require to perform at industry-leading speeds.
Key Responsibilities
  • Architect and maintain integrations for NVIDIA/AMD GPU device plugins and Kubernetes Operators to expose hardware capabilities to the control plane.
  • Configure and optimize high-performance host networking stacks, including RDMA, SR-IOV, RoCEv2, and InfiniBand, ensuring line-rate throughput for distributed AI training.
  • Build and manage automated hardware remediation pipelines using DCGM telemetry to proactively identify, isolate, and reset degraded GPU/NIC components before they impact production jobs.
  • Implement and manage sophisticated GPU slicing technologies (MIG, vGPU) to enable efficient multi-tenant inference workloads and maximize cluster utilization.
  • Profile and tune kernel-level parameters, device drivers, and runtime libraries (CUDA, NCCL) to resolve bottlenecks and optimize containerized AI workloads.
  • Collaborate with the Scheduling and Storage engineering teams to ensure topology-aware placement and efficient data movement across the fabric.
  • Define and enforce operational standards for bare-metal provisioning, BIOS/firmware updates, and OS hardening within the containerized environment.
  • Lead technical investigations into complex performance issues spanning hardware, fabric, and software, providing actionable architectural insights.
  • Mentor team members and drive documentation standards for our evolving AI hardware stack.

Qualifications
  • Bachelor's or Master's degree in Computer Science, Electrical Engineering, or related field.
  • 5+ years of systems engineering experience, with strong proficiency in Linux kernel internals, C, or Go.
  • Hands-on experience with GPU architectures (NVIDIA H100/A100), CUDA runtimes, and distributed networking (RDMA, InfiniBand).
  • Deep understanding of containerized environments and Kubernetes device plugin architecture.
  • Proven track record of operating, debugging, and scaling bare-metal systems in large-scale production or HPC environments.
  • Familiarity with infrastructure automation (e.g., Terraform, Ansible, CI/CD pipelines) for managing hardware lifecycles.
  • Strong problem-solving skills, with the ability to navigate ambiguous performance challenges at the intersection of hardware and software.
  • Excellent communication skills, with a collaborative approach to working across infrastructure, scheduling, and reliability teams.
  • Experience working in high-velocity, high-growth engineering environments is strongly preferred

Bitdeer is committed to providing equal employment opportunities in accordance with country, state, and local laws. Bitdeer does not discriminate against employees or applicants based on conditions such as race, color, gender identity and/or expression, sexual orientation, marital and/or parental status, religion, political opinion, nationality, ethnic background or social origin, social status, disability, age, indigenous status, and union.

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