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Remote Gpu Jobs (NOW HIRING)

Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization * Use ... This is a fully remote role that can be completed on your own schedule. * Projects can be extended ...

Senior Software Engineer - CUDA

Palo Alto, CA · On-site +1

$144K - $189K/yr

Through the application of folding schemes, proof aggregation, and GPU acceleration, we're pushing ... A flexible and innovative remote work environment. * Room for continuous growth and development in ...

... or GPU hosting companies . * Experience building or scaling global compute infrastructure . * Background in HPC, cloud infrastructure, or datacenter operations . Benefits We're a remote-first ...

Senior Software Engineer - CUDA

Palo Alto, CA · On-site +1

$144K - $189K/yr

Through the application of folding schemes, proof aggregation, and GPU acceleration, we're pushing ... A flexible and innovative remote work environment. * Room for continuous growth and development in ...

AI Infrastructure Engineer

New York, NY · Remote

$140K - $165K/yr

  • Medical

  • Dental

  • Vision

  • Life

Our headquarters are in San Francisco (Salesforce Tower), but our team is distributed around the globe and we have a remote-first work culture. We are the leading platform for operating GPU ...

Key Customers Solutions Architect

$64.50 - $85/hr

  • Medical

  • Dental

  • Vision

  • Retirement

Guide customers in optimizing GPU performance for ML training and inference workloads, ensuring ... Remote Work Reimbursement: Up to $85/month for mobile and internet. * Disability & Life Insurance:

Staff AI/ML Infrastructure Engineer

$145K - $160K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

... 500 stipend for remote office setup in first year + $400 each following year * Internet ... Design and maintain GPU and bare metal infrastructure in containerized and physical environments

  • Medical

  • Dental

  • Vision

  • Retirement

The role We are looking for a Customer Engineer to support key and strategic Nebius GPU Cloud ... Remote Work Reimbursement: Up to $85/month for mobile and internet. * Disability & Life Insurance:

Senior Software Engineer - AI Middleware

Austin, TX · On-site +1

$121K - $160K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... and fully remote roles. We are seeking a highly experienced Senior Software Engineer to design ... Improve GPU communication paths including GPU-direct transfers, IPC, and CPU/GPU synchronization.

Showing results 41-60

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.

More about Remote Gpu jobs

What cities are hiring for Remote Gpu jobs?

Cities with the most Remote Gpu job openings:

What are the most commonly searched types of Gpu jobs?

The most popular types of Gpu jobs are:

What states have the most Remote Gpu jobs?

States with the most job openings for Remote Gpu jobs include:

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.

Staff Engineer, Inference Optimizations

DigitalOcean

San Francisco, CA • Remote

$191K - $239K/yr

Full-time

Posted 26 days ago


Job description

DigitalOcean is seeking a Senior Engineer 2 to play a key technical role in our AI Inference Optimization team. DigitalOcean aims to be the Inference Cloud of choice for digitally native companies and you will help ensure we can offer the industry-leading performance for our inference services. You will be responsible for the architectural decisions that maximize throughput and minimize latency for the world's most advanced large models. As an IC leader, you will act as a force multiplier for the engineering organization, solving the most complex bottlenecks in memory bandwidth and compute utilization while guiding the technical roadmap for our high-performance inference fleet.

What You'll Do:
  • Performance Architecture: Lead the technical strategy for benchmarking and performance optimizations at the inference engine and GPU kernel layers, ensuring our infrastructure extracts maximum value from every TFLOP.
  • Deep-Dive Optimization: Engineer solutions for complex performance issues, including attention layer optimizations, memory and precision management, and advanced parallelization across multi-node GPU clusters. 
  • Technological Innovation: Proactively implement cutting-edge optimization techniques to keep DigitalOcean at the forefront of the Gen AI landscape. Some examples of projects you may work on:
    • Improving batch size performance using AMD's AITER library for AMD MI355X - identify and tune AITER's CK (composable kernel) or ASK (assembly) to optimize FP8 / BF16 
    • Identify kernel fusion opportunities for GLM-5 kernels for different layers of the Transformer block (FlashAttention, RMS Norm)
    • Tune expert gateway router kernels for MoE models like Qwen3-235B, DeepSeek V3, GLM-5 etc
  • Hardware & Ecosystem Mastery: Act as the subject matter expert on modern GPU families (NVIDIA/AMD) and their software stacks (CUDA, ROCm, TensorRT, OpenAI Triton), advising on hardware procurement and software integration.
  • Precision Optimization: Develop and deploy state-of-the-art quantization techniques (FP8, INT8, and experimental FP4) to double throughput without losing accuracy.
  • Technical Mentorship: Lead by example through high-quality code and design reviews, elevating the technical bar for the team without the administrative overhead of direct management.
  • Strategic Collaboration: Partner with Product Management and TPMs to translate "theoretical hardware limits" into "shippable product features," ensuring our platform is both powerful and developer-friendly.
  • Community Leadership: Maintain a strong presence in the GPU infrastructure and model performance optimization communities, contributing to and integrating the best of open-source AI.
What You'll Bring to DigitalOcean:
  • Technical Depth: 5+ years of experience in high-performance computing or AI infrastructure, with a proven track record of solving compute utilization and memory bandwidth bottlenecks.
  • Gen AI Literacy: Deep familiarity with the Gen AI (LLM, VLM, LMM) landscape, including the specific quirks and architectural requirements of major model families.
  • Optimization Expert: Hands-on experience with attention-layer optimizations and parallelization strategies across distributed GPU environments.
  • Hardware Fluency: Comprehensive understanding of NVIDIA and AMD GPU architectures and their respective software ecosystems (CUDA, ROCm, etc.).
  • Open Source Mastery: Extensive experience integrating, building with, and contributing to open-source software projects.
  • Systems Design: Excellent system design skills, particularly related to low-level GPU programming - optimization, memory access patterns, and parallel execution.
  • Leadership through Influence: Experience acting as a technical lead, driving design and delivery through cross-functional alignment and expert-level delegation.
  • Low-Level Mastery: Deep understanding of GPU architectures (SMs, Warp scheduling, Tensor Cores).
  • The Toolkit: Expert-level Triton or CUDA. If you've contributed to the Triton compiler or wrote custom CUDA kernels for a major LLM, we want you.
Compensation Range: 
  • $191,200 - $239,000

*This is a remote role

JR: 2026-7625

#LI-Remote