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Remote Gpu Jobs (NOW HIRING)

Weekday AI

CUDA Engineering Expert

$80 - $100/hr

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 ...

Snarkify

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 ...

Runware

Hardware - General Manager

... 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 ...

Snarkify

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 ...

vCluster Labs

AI Infrastructure Engineer

New York, NY · Remote

$150K - $200K/yr

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 ...

Vultr

Staff AI/ML Infrastructure Engineer

$145K - $160K/yr

... 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

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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

GenAI / LLM Engineer - Remote (should be able to work on PST time zones)

Rootshell Enterprise Technologies, Inc.

Remote

Other

Re-posted 21 days ago

Job description

GenAI/LLM Engineer
Remote (should be able to work on PST time zones)
Prefers local to bay area.
Implementing GenAI requires specialized expertise in large language models. Traditional data scientists often haven't had the opportunity to dive deep into the practical intricacies of LLMs-particularly advanced fine-tuning techniques, model compression strategies, memory optimization approaches, and specialized training workflows. This role requires a hands-on deep learning practitioner comfortable with modern frameworks and libraries specific to LLM development.
  • Enables domain-specific fine-tuning of models to Client unique utility context
  • Improves model performance while reducing computational costs through advanced optimization techniques
  • Creates Client-specific AI capabilities that address our unique operational challenges
  • Enables the CoE to move beyond generic AI tools to customized solutions that deliver higher business value

Key Responsibilities:
  • Implement and optimize advanced fine-tuning approaches (LoRA, PEFT, QLoRA) to adapt foundation models to Client domain
  • Develop systematic prompt engineering methodologies specific to utility operations, regulatory compliance, and technical documentation
  • Create reusable prompt templates and libraries to standardize interactions across multiple LLM applications and use cases
  • Implement prompt testing frameworks to quantitatively evaluate and iteratively improve prompt effectiveness
  • Establish prompt versioning systems and governance to maintain consistency and quality across applications
  • Apply model customization techniques like knowledge distillation, quantization, and pruning to reduce memory footprint and inference costs
  • Tackle memory constraints using techniques such as sharded data parallelism, GPU offloading, or CPU+GPU hybrid approaches
  • Build robust retrieval-augmented generation (RAG) pipelines with vector databases, embedding pipelines, and optimized chunking strategies
  • Design advanced prompting strategies including chain-of-thought reasoning, conversation orchestration, and agent-based approaches
  • Collaborate with the MLOps engineer to ensure models are efficiently deployed, monitored, and retrained as needed

Expected Skillset:
  • Deep Learning & NLP: Proficiency with PyTorch/TensorFlow, Hugging Face Transformers, DSPy, and advanced LLM training techniques
  • GPU/Hardware Knowledge: Experience with multi-GPU training, memory optimization, and parallelization strategies
  • LLMOps: Familiarity with workflows for maintaining LLM-based applications in production and monitoring model performance
  • Technical Adaptability: Ability to interpret research papers and implement emerging techniques (without necessarily requiring PhD-level mathematics)
  • Domain Adaptation: Skills in creating data pipelines for fine-tuning models with utility-specific content

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