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Remote Nvidia Engineering Jobs (NOW HIRING)

Loadsmart

Senior Embedded Systems Engineer

OR · Remote

Engineering LOCATION: United States (Remote) WHAT YOU GET TO DO: * Maintain end-to-end system integrity across the SmartGate hardware-software stack: AXIS IP cameras, NVIDIA Jetson edge compute ...

Loadsmart

Senior Embedded Systems Engineer

Chicago, IL · On-site +1

Engineering LOCATION: United States (Remote) WHAT YOU GET TO DO: * Maintain end-to-end system integrity across the SmartGate hardware-software stack: AXIS IP cameras, NVIDIA Jetson edge compute ...

PICTOR LABS INC

$89K - $123K/yr

... NVIDIA hardware, and ensuring our inference infrastructure meets FDA and SOC2 compliance ... Remote US Company: Pictor Labs Employment Type: Full-time Responsibilities * Design, development ...

PRAGMATIKE

CUDA Kernel Engineer

New York, NY · Remote

Remote US Start date: ASAP Languages: English (required) About the Role Pragmatike is hiring on ... engineering best practices. What Were Looking For * Proven track record building NVIDIA CUDA ...

PRAGMATIKE

CUDA Kernel Engineer

Cambridge, MA · Remote

Remote US Start date: ASAP Languages: English (required) About the Role Pragmatike is hiring on ... engineering best practices. What Were Looking For * Proven track record building NVIDIA CUDA ...

PRAGMATIKE

CUDA Kernel Engineer

Atlanta, GA · Remote

Remote US Start date: ASAP Languages: English (required) About the Role Pragmatike is hiring on ... engineering best practices. What Were Looking For * Proven track record building NVIDIA CUDA ...

PRAGMATIKE

CUDA Kernel Engineer

San Francisco, CA · Remote

Remote US Start date: ASAP Languages: English (required) About the Role Pragmatike is hiring on ... engineering best practices. What Were Looking For * Proven track record building NVIDIA CUDA ...

PRAGMATIKE

CUDA Kernel Engineer

Chicago, IL · Remote

Remote US Start date: ASAP Languages: English (required) About the Role Pragmatike is hiring on ... engineering best practices. What Were Looking For * Proven track record building NVIDIA CUDA ...

PRAGMATIKE

CUDA Kernel Engineer

Austin, TX · Remote

Remote US Start date: ASAP Languages: English (required) About the Role Pragmatike is hiring on ... engineering best practices. What Were Looking For * Proven track record building NVIDIA CUDA ...

Cybercoders

Senior Platform Engineer

San Francisco, CA · Remote

$175K - $275K/yr

FULLY remote! Salary: $175k-$275k base + RSUs + Full Benefits Requirements: 5+ years in Systems Engineering or HPC Infrastructure, strong Linux and bare-metal GPU experience, NVIDIA DGX/HGX ...

United IT Solutions

GPU Infra Engineer

Irving, TX · Remote

Remote * Experience with bare metal and over-all architecture required GPU Bare Metal - Required ... Network engineering experience with VyOS platforms. What You'll Be Working On: * Provisioning and ...

Hugging Face

Cloud ML DevRel Engineer - US remote

New York, NY · On-site +1

$61 - $81.50/hr

... NVIDIA, AMD, Intel Gaudi, AWS Inferentia, TPU), and systems partners (Dell, Nutanix), to make it ... This is a solid engineering role with a strong flavor of education and community. Your impact comes ...

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Remote Nvidia Engineering information

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$57K

$137K

$197K

How much do remote nvidia engineering jobs pay per year?

As of Jun 12, 2026, the average yearly pay for remote nvidia engineering in the United States is $137,006.00, according to ZipRecruiter salary data. Most workers in this role earn between $121,500.00 and $151,500.00 per year, depending on experience, location, and employer.

What is a Remote Nvidia Engineer?

A Remote Nvidia Engineer is a professional who works for Nvidia, or with Nvidia technologies, from a location outside of a traditional office setting. These engineers may specialize in areas such as GPU development, AI research, software engineering, or hardware design, and they collaborate with teams virtually. Remote Nvidia Engineers use digital tools to communicate, manage projects, and contribute to cutting-edge technologies in graphics processing, artificial intelligence, and computing platforms. The remote aspect allows for flexible work arrangements and the ability to participate in global projects.

What are some common challenges faced by engineers working remotely for Nvidia, and how can they be overcome?

Remote engineers at Nvidia often encounter challenges related to communication across time zones, staying aligned with fast-paced project developments, and maintaining visibility within distributed teams. To overcome these, it's important to proactively engage in virtual meetings, leverage collaboration tools like Slack and Jira, and regularly update your team on progress. Building strong relationships with peers and seeking out mentorship opportunities can also help remote engineers stay connected and advance within the company.

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

To excel as a Remote Nvidia Engineer, you typically need a strong background in computer engineering, programming (e.g., C++, Python), and experience with GPU architectures, often supported by a relevant degree. Familiarity with Nvidia tools like CUDA, cuDNN, and deep learning frameworks, as well as proficiency in remote collaboration platforms, are crucial. Strong problem-solving skills, self-motivation, and effective communication are vital soft skills for working independently and collaborating across distributed teams. These competencies ensure efficient development, troubleshooting, and innovation in Nvidia's complex, high-performance computing environments.

What is the difference between Remote Nvidia Engineering vs Remote Nvidia Data Scientist?

AspectRemote Nvidia EngineeringRemote Nvidia Data Scientist
Required CredentialsBachelor's in Engineering, Computer Science, or related field; experience with GPU programmingBachelor's or higher in Data Science, Statistics, or related; proficiency in machine learning and data analysis
Work EnvironmentDesign, develop, and optimize GPU hardware/software; collaborative teamsAnalyze large datasets, develop models, and generate insights; often cross-functional teams
Employer & Industry UsagePrimarily in hardware, AI, and high-performance computing sectorsPrimarily in AI, analytics, and research sectors

Remote Nvidia Engineering focuses on hardware and software development for GPUs, requiring engineering credentials and technical skills. Remote Nvidia Data Scientists analyze data and build models, requiring expertise in data science. Both roles are remote, but they serve different functions within Nvidia's ecosystem.

More about Remote Nvidia Engineering jobs
What cities are hiring for Remote Nvidia Engineering jobs? Cities with the most Remote Nvidia Engineering job openings:
What are the most commonly searched types of Nvidia Engineering jobs? The most popular types of Nvidia Engineering jobs are:
What states have the most Remote Nvidia Engineering jobs? States with the most job openings for Remote Nvidia Engineering jobs include:
What job categories do people searching Remote Nvidia Engineering jobs look for? The top searched job categories for Remote Nvidia Engineering jobs are:
Remote Nvidia Engineering jobs near you
Infographic showing various Remote Nvidia Engineering job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% Remote job distribution, with an average salary of $137,006 per year, or $65.9 per hour.
Golang Developer with Devops/LLM exp

Golang Developer with Devops/LLM exp

Noblesoft Technologies

San Francisco, CA • Remote

Contractor

Posted 2 days ago

Job description

Job Title: Golang Developer with Devops/LLM exp
Location: California (Remote)
 
Job Description:
We are looking for devs with general cloud services / distributed services experience, with LLM experience as a secondary skill. GPU experience is now low on the list of preferred skills: Dedicated Inference Service
Required Skills-
  • Deep experience building services in modern cloud environments on distributed systems (i.e., containerization (Kubernetes, Docker), infrastructure as code, CI/CD pipelines, APIs, authentication and authorization, data storage, deployment, logging, monitoring, alerting, etc.)
  • Experience working with Large Language Models (LLMs), particularly hosting them to run inference
  • Strong verbal and written communication skills. Your job will involve communicating with local and remote colleagues about technical subjects and writing detailed documentation.
  • Experience with building or using benchmarking tools for evaluating LLM inference for various models, engine, and GPU combinations.
  • Familiarity with various LLM performance metrics such as prefill throughput, decode throughput, TPOT, and TTFT
  • Experience with one or more inference engines: e.g., vLLM, SGLang, and Modular Max
  • Familiarity with one or more distributed inference serving frameworks: e.g., llm-d, NVIDIA Dynamo, and Ray Serve etc.
  • Experience with AMD and NVIDIA GPUs, using software like CUDA, ROCm, AITER, NCCL, RCCL, etc.
  • Knowledge of distributed inference optimization techniques - tensor/data parallelism, KV cache optimizations, smart routing etc.
What You'll Be Working On-
  • Develop and maintain an inference platform for serving large language models optimized for the various GPU platforms they will be run on.
  • Work on complex AI and cloud engineering projects through the entire product development lifecycle (PDLC) - ideation, product definition, experimentation, prototyping, development, testing, release, and operations.
  • Build tooling and observability to monitor system health, and build auto tuning capabilities.
  • Build benchmarking frameworks to test model serving performance to guide system and infrastructure tuning efforts.
  • Build native cross platform inference support across NVIDIA and AMD GPUs for a variety of model architectures.
  • Contribute to open source inference engines to make them perform better on DigitalOcean cloud.