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Remote Nvidia Engineering Jobs (NOW HIRING)

Weekday AI

CUDA Engineering Expert

$80 - $100/hr

Experience optimizing kernels for NVIDIA Blackwell hardware is a plus * Familiarity with NSight ... This is a fully remote role that can be completed on your own schedule. * Projects can be extended ...

Open Select

Member of Engineering, Backend - AI

$290K/yr

Fully remote (East Coast preferred) Salary: Up to $290k + equity Industry: AI, Developer Platform ... Unmatched funding: $626M raised (Seed/Series A/B), backed by Bain Capital, Nvidia, Redpoint ...

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

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 ...

Apply Early

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 ...

Apply Early

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 ...

Apply Early

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 ...

Apply Early

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 ...

Apply Early

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 ...

Apply Early

Quantiphi, Inc.

Architect - Platform Engineer

Quantiphi is an award-winning, AI-First global digital engineering company that helps the world ... US East/Canada (Remote) Role Overview: We are looking for a highly skilled Architect - Platform ...

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All JobsRemote Nvidia Engineering Jobs

Remote Nvidia Engineering information

See salary details

$57K

$137K

$197K

How much do remote nvidia engineering jobs pay per year?

As of Jul 3, 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 1% As Needed, 14% Full Time, 74% Part Time, 4% Temporary, and 7% Nights. Highlights an 88% Physical, 6% Hybrid, and 6% Remote job distribution, with an average salary of $137,006 per year, or $65.9 per hour.
GPU Programming Expert - Fully Remote | Upto $120/hr

GPU Programming Expert - Fully Remote | Upto $120/hr

Mercor

San Francisco, CA • Remote

$120/hr

Full-time

Posted 28 days ago

Be an early applicant

Job description

About the job

Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

Position: CUDA Engineering Expert
Type: Contract
Compensation: $80–$120/hour
Location: Remote

Role Responsibilities

  • Analyze and optimize GPU kernels for performance, efficiency, and hardware utilization.
  • Use profiler metrics like L2 cache hit rate, L2 throughput, and occupancy to guide kernel improvements.
  • Review GPU kernel implementations to identify bottlenecks without needing extensive algorithmic background.
  • Write, modify, and reason about C++17, Python, and GPU programming code.
  • Apply CUDA, HIP, and shader programming expertise to improve performance outcomes.
  • Document optimization decisions clearly, noting when specific profiler metrics are useful.

Qualifications

Must-Have

  • Available to work at least 20 hrs/wk.
  • Fluent in core C++ features through C++17.
  • Working knowledge of Python and Git.
  • Fluent in at least one GPU programming model like CUDA, HIP, Slang, HLSL, or GLSL.
  • At least 1 year of professional or graduate-level research experience with GPUs.
  • Strong understanding of GPU profiler performance metrics for kernel optimization.
  • Ability to optimize GPU kernels without deep prior context on every algorithm.

Preferred

  • Experience with CUDA, HIP, CUDA C++ Core Libraries, inline PTX assembly, or tensor core-level optimization.
  • Experience optimizing kernels for NVIDIA Blackwell hardware.
  • Familiarity with NSight Compute.
  • Prior experience with GPU hardware organizations like NVIDIA, AMD, or Qualcomm.
  • Open-source contributions related to GPU kernel optimization.

Application Process (Takes 20–30 mins to complete)

  • Submit your resume or relevant technical background to get started.
  • Qualified applicants may be asked to complete a brief technical assessment or submit additional information.

Resources & Support

  • For details about the interview process and platform information, please check: https://talent.docs.mercor.com/welcome
  • For any help or support, reach out to: support@mercor.com

PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.