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Cloud Gpu Jobs (NOW HIRING)

Specter

$200 - $250/hr

Productionize models from the research team, spanning containerization, inference optimization, and deployment to edge devices and cloud GPU infrastructure. * Build and own offline and online ...

RadixArk

Performance Engineer

Palo Alto, CA · On-site

... cloud environments • Optimize latency, throughput, memory usage, batching, scheduling, routing, and GPU utilization • Investigate performance regressions in real customer environments • Work ...

RadixArk

Performance Engineer

Palo Alto, CA · On-site

... cloud environments • Optimize latency, throughput, memory usage, batching, scheduling, routing, and GPU utilization • Investigate performance regressions in real customer environments • Work ...

General Robotics

Systems Engineer

Redmond, WA · On-site

Responsibilities : • Design, build, and optimize systems infrastructure spanning edge devices to cloud GPU clusters for robotics workloads. • Develop and maintain low-latency, high-throughput ...

Edgeuno

Managing Director, EdgeUno Compute

Miami, FL · On-site

$150 - $200/hr

Role Overview We are looking for a Managing Director, EdgeUno Compute, to lead and scale our GPU, bare metal, and cloud infrastructure business across Latin America and the Americas. This role ...

Webhosting

$80 - $100/hr

With 33 global cloud data center locations, Vultr is trusted by hundreds of thousands of active customers across 185 countries for its flexible, scalable, global Cloud Compute, Cloud GPU, Bare Metal ...

Triwill Group

$200 - $250/hr

Drive neo cloud delivery programs -- manage capacity delivery from GPU cloud and neo cloud partners (e.g., colocation/bare-metal/GPU cloud providers), including contract milestones, capacity ramps ...

New

Neura Market

$200 - $250/hr

Drive neo cloud delivery programs -- manage capacity delivery from GPU cloud and neo cloud partners (e.g., colocation/bare-metal/GPU cloud providers), including contract milestones, capacity ramps ...

New

Vultr

Staff AI/ML Infrastructure Engineer

$145K - $160K/yr

With 33 global cloud data center locations, Vultr is trusted by hundreds of thousands of active customers across 185 countries for its flexible, scalable, global Cloud Compute, Cloud GPU, Bare Metal ...

Vultr

Senior Technical Product Manager, Observability

$130K - $165K/yr

With 33 global cloud data center locations, Vultr is trusted by hundreds of thousands of active customers across 185 countries for its flexible, scalable, global Cloud Compute, Cloud GPU, Bare Metal ...

Vultr

Software Engineer, Core Cloud Engineering

$80K - $95K/yr

With 33 global cloud data center locations, Vultr is trusted by hundreds of thousands of active customers across 185 countries for its flexible, scalable, global Cloud Compute, Cloud GPU, Bare Metal ...

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Cloud Gpu information

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How much do cloud gpu jobs pay per hour?

As of Sep 9, 2026, the average hourly pay for cloud gpu in the United States is $61.71, according to ZipRecruiter salary data. Most workers in this role earn between $54.09 and $74.04 per hour, depending on experience, location, and employer.

What is a cloud GPU?

Cloud GPU jobs refer to computational tasks that utilize Graphics Processing Units (GPUs) hosted on cloud platforms. These jobs can include machine learning, data analysis, graphics rendering, and scientific simulations, all executed remotely on powerful GPU hardware provided by cloud service providers. Using cloud GPUs allows users to scale their computing resources on demand without investing in expensive physical GPUs, making it cost-effective and flexible for businesses and researchers alike.

What skills and qualifications are needed to work with cloud GPUs?

To thrive as a Cloud GPU Engineer, you need a strong background in computer science, cloud computing, and GPU architecture, often supported by a degree in a relevant field and experience with parallel programming. Proficiency with cloud platforms (such as AWS, Azure, or Google Cloud), GPU management tools (like NVIDIA CUDA), and containerization systems (like Docker or Kubernetes) is typically required. Strong problem-solving skills, teamwork, and effective communication help you collaborate and address complex technical challenges. These skills and qualities are essential for optimizing GPU resources, ensuring high performance, and supporting scalable cloud-based solutions.

What are the main challenges faced when managing cloud GPU resources in a production environment?

One of the primary challenges in managing cloud GPU resources is optimizing usage to balance performance and cost, since GPUs can be expensive if left underutilized. Additionally, workloads often require careful scheduling and monitoring to ensure resource availability and prevent bottlenecks, especially when supporting multiple teams or projects. Security and data compliance can also be more complex due to the shared infrastructure in cloud environments. Collaborating effectively with DevOps, engineering, and data science teams is crucial to align resource allocation with project needs and timelines.

What is the difference between Cloud Gpu vs Data Scientist?

AspectCloud GpuData Scientist
Required CredentialsKnowledge of cloud platforms, GPU computing, and basic programmingDegree in data science, statistics, or related field; often Python or R skills
Work EnvironmentCloud-based infrastructure, hardware management, and GPU resourcesData analysis, modeling, and visualization in office or remote settings
Industry UsageTech, AI, machine learning, and high-performance computingBusiness, finance, healthcare, and research sectors

While Cloud Gpu specialists focus on managing GPU resources in cloud environments for high-performance tasks, Data Scientists analyze data to extract insights and build models. Both roles often collaborate in AI projects but differ in technical focus and daily tasks.

What other helpful pages are available for Cloud Gpu?

Other pages related to Cloud Gpu:

Infographic showing various Cloud Gpu job openings in the United States as of September 2026, with employment types broken down into 92% Full Time, 3% Part Time, and 5% Contract. Highlights an 76% Physical, 6% Hybrid, and 18% Remote job distribution, with an average salary of $128,365 per year, or $61.7 per hour.

Principal System Software Architect, AI/GPU Platforms

Austin, TX • Hybrid

Advanced Micro Devices, Inc
Computer and Electronic Product Manufacturing • 5 - 10K employees
Learn more about Advanced Micro Devices, Incexternal

Full-time

Posted 20 days ago

Advanced Micro Devices rating

8.6

Company rating: 8.6 out of 10

Based on 13 frontline employees who took The Breakroom Quiz

28th of 162 rated electronics manufacturers

Job description


WHAT YOU DO AT AMD CHANGES EVERYTHING 

At AMD, our mission is to build great products that accelerate next-generation computing experiences—from AI and data centers, to PCs, gaming and embedded systems. Grounded in a culture of innovation and collaboration, we believe real progress comes from bold ideas, human ingenuity and a shared passion to create something extraordinary. When you join AMD, you’ll discover the real differentiator is our culture. We push the limits of innovation to solve the world’s most important challenges—striving for execution excellence, while being direct, humble, collaborative, and inclusive of diverse perspectives. Join us as we shape the future of AI and beyond.  Together, we advance your career.  



THE ROLE

You will join the system software architecture team behind AMD Instinct™ accelerators, the GPUs powering some of the world's largest AI and HPC deployments. This role is focused on next-generation, rack-scale AI platforms in the MI400 class spanning the GPU, the node, and the scale-up/scale-out fabric that binds thousands of accelerators into a single training and inference system.

THE PERSON

As a Systems Software Architect, you sit at the intersection of silicon, firmware, driver, runtime, and framework. You define how the software stack exposes and orchestrates the hardware so that AMD's largest customers can extract maximum performance, reliability, and utilization from their infrastructure.

KEY RESPONSIBILITIES
  • Own the end-to-end system software architecture for one or more MI400-class subsystems for example GPU memory management, scheduling and queuing, RAS and serviceability, virtualization/partitioning (SR-IOV), or the scale-up/scale-out interconnect software model.
  • Drive architecture across the stack: kernel-mode driver (amdgpu/KFD), user-mode runtime (ROCr/HSA), firmware interfaces, and the ROCm software platform, ensuring the layers compose cleanly and perform.
  • Partner with silicon and SoC architects during pre-silicon definition to shape hardware/software interfaces, programming models, and register/firmware contracts before tape-out.
  • Define the software strategy for multi-GPU and rack-scale topologies, including Infinity Fabric / UALink-style interconnect, collective communication (RCCL), memory coherence, and address translation across the platform.
  • Establish architecture for reliability, availability, and serviceability at scale, error detection, containment, telemetry, recovery, and graceful degradation across large clusters.
  • Set direction on performance: identify bottlenecks in the launch path, memory subsystem, and communication path, and define the software mechanisms to close them.
  • Produce architecture specifications, reference designs, and design reviews that align firmware, driver, runtime, and framework teams onto a shared plan.
  • Act as a technical anchor across AMD and with strategic hyperscale and AI customers — translating their workload requirements into architectural direction and representing AMD in deep technical engagements.

PREFERRED EXPERIENCE

  • Linux Memory Management and Heterogeneous Memory Management (HMM).
  • GPU / DRM driver development.
  • Cache coherence and memory consistency protocols.
  • GPU Networking technologies including including scale up transport, NVLink, UALink, RDMA, and peer-direct.
  • Scale-up and scale-out networking, and communication collectives (e.g., RCCL/NCCL), MPI, or SHMEM.
  • Data-center fabrics such as Infinity Fabric, UALink, Ultra Ethernet, InfiniBand, and RoCE, with topology-aware software.

DESIRABLE EXPERIENCE

  • Direct experience with the ROCm stack, AMD Instinct, CUDA, or comparable GPU compute ecosystems.
  • Hands-on experience developing or optimizing GPU compute kernels (HIP, CUDA, Triton, or assembly-level tuning).
  • Experience with deep learning frameworks like  PyTorch and TensorFlow in particular including framework integration, custom operators, and performance tuning on GPU backends.
  • Familiarity with the broader ML framework and compiler ecosystem (PyTorch, JAX, TensorFlow, ONNX, MLIR/compiler stacks).
  • Hands-on experience with GPU compute technologies such as OpenCL and Vulkan.
  • Familiarity with AI/ML training and inference workloads (transformers, large-scale distributed training, KV-cache and memory pressure, inference serving).
  • Background in virtualization, multi-tenancy, confidential computing, or cloud GPU provisioning.
  • Contributions to open-source kernel, driver, or runtime projects.

ACADEMIC CREDENTIALS: 

  • Bachelor’s or Master’s in Electrical Engineer, Computer Engineering, Computer Science, or a closely related field

LOCATION:

Austin, TX

Santa Clara, CA

This role is not eligible for visa sponsorship.

#LI-BW2

#LI-HYBRID



Benefits offered are described:  AMD benefits at a glance.

AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law.   We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.

AMD may use Artificial Intelligence to help screen, assess or select applicants for this position.  AMD’s “Responsible AI Policy” is available here.

 

This posting is for an existing vacancy.

Qualifications:

Benefits offered are described:  AMD benefits at a glance.

AMD does not accept unsolicited resumes from headhunters, recruitment agencies, or fee-based recruitment services. AMD and its subsidiaries are equal opportunity, inclusive employers and will consider all applicants without regard to age, ancestry, color, marital status, medical condition, mental or physical disability, national origin, race, religion, political and/or third-party affiliation, sex, pregnancy, sexual orientation, gender identity, military or veteran status, or any other characteristic protected by law.   We encourage applications from all qualified candidates and will accommodate applicants’ needs under the respective laws throughout all stages of the recruitment and selection process.

AMD may use Artificial Intelligence to help screen, assess or select applicants for this position.  AMD’s “Responsible AI Policy” is available here.

 

This posting is for an existing vacancy.

Education:UNAVAILABLEEmployment Type: FULL_TIME

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