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Remote Gpu Jobs in Nevada (NOW HIRING)

Motional

Machine Learning Systems Engineer

Las Vegas, NV · On-site +1

$144K - $192K/yr

Design and maintain high-performance GPU kernels in Triton or CUDA for state-of-the-art ML ... be fully remote. The salary range for this role is an estimate based on a wide range of ...

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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.
What are popular job titles related to Remote Gpu jobs in Nevada? For Remote Gpu jobs in Nevada, the most frequently searched job titles are:
What job categories do people searching Remote Gpu jobs in Nevada look for? The top searched job categories for Remote Gpu jobs in Nevada are:
What cities in Nevada are hiring for Remote Gpu jobs? Cities in Nevada with the most Remote Gpu job openings:
Remote Gpu jobs near you
Infographic showing various Remote Gpu job openings in Nevada as of July 2026, with employment types broken down into 2% Locum Tenens, 87% Full Time, 4% Part Time, 1% Temporary, 2% Contract, and 4% Nights. Highlights an 84% Physical, 6% Hybrid, and 10% Remote job distribution.
Software Engineer, ML Dev Enablement

Software Engineer, ML Dev Enablement

Motional

Las Vegas, NV • On-site, Remote

Other

Posted 29 days ago

Job description

Mission Summary:

We are looking for a Software Engineer to join our ML Infrastructure: Dev Enablement Team. Our mission is to build a frictionless development environment that empowers our researchers and engineers to rapidly innovate on deep learning models for autonomous driving.

We manage a high-scale Cloud Development Environment (CDE) platform that provides standardized, high-performance workspaces for ML development. As we evolve, in this role, you'll spearhead high-impact initiatives: designing multi-cloud setups to maximize GPU availability, driving deep-level model optimization, and building next-generation Agentic AI toolings. You will play a pivotal role in ensuring our training ecosystem remains cutting-edge, resilient and highly efficient.

What You'll Be Doing:

  • Build Agentic AI Tooling: Design, develop, and enhance Agentic AI tools and systems to automate workflows, streamline the ML lifecycle, and empower developer productivity.
  • Scale Core Infrastructure: Drive the continuous development of our core ML infrastructure and existing CDE platform, leveraging Kubernetes to build robust, high-scale distributed solutions.
  • System-Level ML Optimization: Partner closely with ML Researchers to profile and optimize distributed training jobs (PyTorch/DDP) and data pipelines. Focus on resolving system-level bottlenecks-such as data loading (I/O), memory management, and network communication overhead-to maximize GPU utilization and training throughput.
  • Collaborate Cross-Functionally: Partner with ML engineers and data scientists to understand their complex needs, bridging the gap between underlying infrastructure and model development.

What We're Looking For:

  • BS or MS in Computer Science or related field
  • Strong knowledge of software engineering principles and distributed systems.
  • Strong proficiency with Python or Go or C++
  • Experience with building on AWS services or other Cloud platforms and container orchestration using Kubernetes.
  • Experience with the various stages of the ML development lifecycle
Bonus Points:
  • Hands-on experience with ML model profiling and performance optimization for distributed training.
  • Experience managing or working with high-performance compute resources (GPUs).
  • Experience with ML frameworks such as PyTorch or Ray.
  • Experience building, integrating, or enhancing Agentic AI systems and LLM-driven developer tools.

 We encourage a hybrid schedule with in-office time at our Las Vegas location to support collaboration, or this role can be fully remote.

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