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Remote 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.

Mercor

CUDA Engineer - Kernel Optimization

San Francisco, CA · Remote

$241K/yr

Remote Role Responsibilities * Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization. * Use profiler metrics like L2 cache hit rate and occupancy to guide kernel ...

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Remote Gpu information

What is a remote GPU?

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 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.

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 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.

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Remote Gpu jobs near you
Infographic showing various Remote Gpu job openings in the United States as of August 2026, with employment types broken down into 50% Full Time, and 50% Contract. Highlights an 100% Remote job distribution.

Member of Technical Staff (GPU Performance Engineer)

Reka

Remote

Full-time

Medical, Dental, Vision, PTO

Re-posted 12 days ago

Job description

We are seeking an experienced GPU Performance Engineer with a strong background in Python and large-scale model training. In this role, you will design and implement improvements to our training infrastructure and directly contribute to technical decisions that optimize performance of our models. You will also work on post-training processes, including reinforcement learning and fine-tuning. Furthermore, you will contribute to improving the efficiency and scalability of our model serving infrastructure.
Ideal Experience
  • Strong engineering skills with fluency in Python and PyTorch (or other frameworks).
  • Proven experience implementing and training large deep learning models.
  • Experience writing and debugging low-level GPU code (CUDA, C++).
  • Experience scaling up GPU jobs using large-scale compute clusters (e.g., Slurm or Kubernetes).
  • Demonstrated ability to analyze and optimize the performance of GPU-accelerated workloads, including profiling, identifying bottlenecks, and implementing performance tuning techniques.

Reka's Mission
Reka's mission is to build useful multimodal artificial intelligence and use it to empower organizations and businesses. We are a globally distributed foundation model startup, headquartered in the San Francisco Bay Area, California. Embracing a remote-first approach, our team brings together top talent from around the world. Our founding team, along with many of our team members, has contributed to numerous breakthroughs in AI over the past decade.
Why Reka?
  • An Elite Team: Collaborate with top-tier engineers, researchers, and operators from renowned organizations like Google DeepMind, Facebook AI Research (FAIR), and successful startups, driving innovation in AI technology.
  • Cutting-edge Infrastructure: Train state-of-the-art models leveraging the latest software and hardware, expanding the frontier of innovation in AI infrastructure development.
  • Inclusive and Open Culture: Thrive in an open and inclusive work environment that values diverse perspectives and fosters creativity.
  • Generous Benefits: Enjoy five weeks of paid leave to recharge, comprehensive healthcare benefits (including vision and dental), and additional perks that support your well-being.
  • Visa Support: We provide visa assistance, including H1B and OPT transfers, for US employees to ensure a smooth transition and support your career with us.

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