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Remote Gpu Jobs (NOW HIRING)

Futran Tech Solutions Pvt. Ltd.

GPU Software Engineer

$138K - $185K/yr

USA(Remote) Role Summary We are seeking expert-level GPU Software Engineers to support a high-visibility platform initiative within the Maya program, focused on building software tooling on top of a ...

Vultr

Strategic Technical Account Manager GPU

$115K - $140K/yr

... Cloud GPU, Bare Metal, and Cloud Storage solutions. In December 2024 Vultr announced an equity ... remote office setup in first year + $400 each following year Internet reimbursement up to $75 per ...

Nebius

GPU Cluster Architect

$184K - $318K/yr

From large-scale GPU orchestration to inference optimization, we own the hard problems across ... Remote work reimbursement: Up to $85/month for mobile and internet. * Disability & life insurance

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.

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Remote Gpu information

What are the key skills and qualifications needed to thrive as a Remote GPU Engineer, and why are they important?

To thrive as a Remote GPU Engineer, you need a strong background in computer science, GPU architectures, parallel programming (CUDA/OpenCL), and relevant software development experience. Familiarity with tools like NVIDIA CUDA Toolkit, profiling/debugging utilities, and cloud-based GPU platforms (e.g., AWS, Azure) is essential, along with certifications in GPU computing as a plus. Excellent problem-solving, communication, and self-motivation are critical soft skills for collaborating remotely and handling complex technical challenges. Mastery of these skills ensures efficient design, optimization, and deployment of high-performance GPU solutions in distributed environments.

What are Remote GPUs?

Remote GPUs are graphics processing units that are hosted on remote servers and accessed over the internet, rather than being physically installed in your local computer. They enable users to perform high-performance computing tasks such as machine learning, rendering, or data analysis without investing in expensive hardware. Remote GPUs are commonly used in cloud computing environments, making powerful GPU resources accessible on-demand and scalable according to project needs.

What is the difference between Remote Gpu vs Remote Data Scientist?

AspectRemote GpuRemote Data Scientist
Required CredentialsGPU programming certifications, CUDA, OpenCLStatistics, machine learning, programming (Python, R)
Work EnvironmentHigh-performance computing, hardware access, cloud GPU servicesData analysis, modeling, visualization
Industry UsageAI, deep learning, graphics renderingBusiness analytics, research, AI development

Remote Gpu roles focus on GPU programming and hardware utilization for AI and graphics tasks, often requiring technical certifications. Remote Data Scientists analyze data, build models, and interpret results, typically with programming and statistical skills. While both roles may work remotely and in tech industries, their core skills and tools differ significantly.

What are some common challenges faced by professionals working in Remote GPU roles, and how can they be addressed?

Professionals in Remote GPU roles often encounter challenges such as managing latency, ensuring data security, and optimizing resource allocation across distributed systems. Effective communication and collaboration with cross-functional teams—including software developers, data scientists, and IT administrators—are essential to address these issues. Staying updated with the latest GPU virtualization technologies and best practices can also help professionals troubleshoot performance bottlenecks and maintain seamless remote access to GPU resources.
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Remote Gpu jobs near you
GPU Programming Expert - Fully Remote | Upto $120/hr

GPU Programming Expert - Fully Remote | Upto $120/hr

Mercor

San Francisco, CA • Remote

$120/hr

Full-time

Re-posted 5 days ago

Job description

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position: CUDA Engineering Expert
Type: Contract
Compensation: $80–$120/hour
Location: Remote

Role Responsibilities

  • Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization.
  • Use profiler metrics like L2 cache hit rate, L2 throughput, and occupancy to guide kernel improvements.
  • Review GPU kernel implementations to identify bottlenecks without needing extensive algorithmic background.
  • Write, modify, and reason about C++17, Python, and GPU programming code.
  • Apply CUDA, HIP, and shader programming expertise to improve performance outcomes.
  • Document optimization decisions clearly, noting when specific profiler metrics are useful.

Qualifications

Must-Have

  • Available to work at least 20 hrs/wk.
  • Fluent in core C++ features through C++17.
  • Working knowledge of Python and Git.
  • Fluent in at least one GPU programming model like CUDA, HIP, Slang, HLSL, or GLSL.
  • At least 1 year of professional or graduate-level research experience with GPUs.
  • Strong understanding of GPU profiler performance metrics for kernel optimization.
  • Ability to optimize GPU kernels without deep prior context on every algorithm.

Preferred

  • Experience with CUDA, HIP, CUDA C++ Core Libraries, inline PTX assembly, or tensor core-level optimization.
  • Experience optimizing kernels for NVIDIA Blackwell hardware.
  • Familiarity with NSight Compute.
  • Prior experience with GPU hardware organizations like NVIDIA, AMD, or Qualcomm.
  • Open-source contributions related to GPU kernel optimization.

Application Process (Takes 20–30 mins to complete)

  • Submit your resume or relevant technical background to get started.
  • Qualified applicants may be asked to complete a brief technical assessment or submit additional information.

Resources & Support

  • For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
  • For any help or support, reach out to: support@mercor.com

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.