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Cuda Remote Jobs (NOW HIRING)

Weekday AI

CUDA Engineering Expert

$80 - $100/hr

Apply CUDA, HIP, shader programming, or related kernel programming expertise to improve performance ... 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

Proficient in GPU programming languages such as CUDA. * Strong understanding of computer ... A flexible and innovative remote work environment. * Room for continuous growth and development in ...

Snarkify

Senior Software Engineer - CUDA

Palo Alto, CA · On-site +1

$144K - $189K/yr

Proficient in GPU programming languages such as CUDA. * Strong understanding of computer ... A flexible and innovative remote work environment. * Room for continuous growth and development in ...

Danaher

Senior Software Engineer - USA Remote

Raleigh, NC · Remote

$119K - $157K/yr

This fully remote position is part of the Leica Microsystems Research and Development organization ... GPU programming using CUDA; Targeting ARM & X86 processing environments; User Interface design;

Near AI

LLM Inference Engineer

San Francisco, CA · On-site +1

San Francisco or Remote About The Role The NEAR AI team is building decentralized and confidential ... CuTe, CUDA, etc. * Proven track record in designing and maintaining end-to-end high-traffic LLM ...

Targeted Talent

Senior Deep Learning Engineer

Austin, TX · On-site +1

$130K - $180K/yr

Knowledge of CUDA/OpenGL * Experience deploying neural networks in production * Familiarity with model compression techniques like quantization, pruning, etc. These are permanent full time remote ...

Danaher

Senior Software Engineer - USA Remote

Durham, NC · Remote

$118K - $156K/yr

This fully remote position is part of the Leica Microsystems Research and Development organization ... GPU programming using CUDA; Targeting ARM & X86 processing environments; User Interface design;

Danaher

Senior Software Engineer - USA Remote

$125K - $165K/yr

This fully remote position is part of the Leica Microsystems Research and Development organization ... GPU programming using CUDA; Targeting ARM & X86 processing environments; User Interface design;

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Cuda Remote information

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How much do cuda remote jobs pay per hour?

As of Jul 11, 2026, the average hourly pay for cuda remote in the United States is $39.36, according to ZipRecruiter salary data. Most workers in this role earn between $16.83 and $57.45 per hour, depending on experience, location, and employer.

Are CUDA programmers in demand?

CUDA programmers are in high demand in fields such as artificial intelligence, data science, and high-performance computing due to their expertise in parallel programming and GPU acceleration. Companies seek professionals with skills in CUDA, C++, and related tools to optimize computational tasks, and job opportunities are growing across various industries that require intensive data processing. Certifications and experience with GPU architectures can enhance employability in this specialized field.

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

To excel as a CUDA Remote Developer, you need strong programming skills in C/C++ and parallel computing concepts, typically supported by a degree in computer science or related field. Familiarity with NVIDIA CUDA Toolkit, GPU architectures, and related development environments is essential. Excellent problem-solving, communication, and self-motivation skills help you collaborate effectively and manage remote work challenges. These competencies ensure efficient development of high-performance GPU-accelerated applications and productive teamwork in distributed settings.

What are some common challenges faced by Cuda Remote developers when working with distributed GPU workloads?

Cuda Remote developers often encounter challenges related to optimizing data transfer between remote devices, managing synchronization across distributed systems, and debugging performance issues that arise due to network latency. Collaborating with cross-functional teams, such as data scientists and DevOps engineers, is essential to ensure efficient GPU resource allocation and seamless integration with existing infrastructures. Staying up to date with the latest CUDA libraries and best practices is also important for overcoming these hurdles and delivering scalable, high-performance solutions.

What jobs use CUDA?

Jobs that use CUDA typically include roles in GPU programming, machine learning, deep learning, data science, and high-performance computing. These positions often require knowledge of parallel programming, C++, and NVIDIA's CUDA toolkit to optimize software for GPU acceleration.

Does Nvidia offer remote positions?

Nvidia offers remote positions for various roles, including technical and engineering jobs like CUDA Remote. These positions often require specific skills, such as programming in CUDA and experience with GPU computing, and may be available in flexible or fully remote work environments depending on the role and team needs.

What is the difference between Cuda Remote vs Data Analyst?

AspectCuda RemoteData Analyst
Required CredentialsTechnical certifications, remote work experienceDegree in statistics, data science, or related field
Work EnvironmentRemote, often project-basedOffice or remote, depending on employer
Industry UsageTech, finance, healthcareBusiness, marketing, finance
Common Search/ComparisonRemote tech rolesData analysis jobs

While Cuda Remote focuses on remote technical roles often involving CUDA programming, Data Analysts primarily analyze data to inform business decisions. Both roles may require analytical skills, but Cuda Remote emphasizes technical CUDA expertise in remote settings, whereas Data Analysts focus on data interpretation and visualization, often in office or hybrid environments.

What are CUDA Remote jobs?

CUDA Remote jobs are positions that focus on developing, optimizing, or supporting applications using NVIDIA's CUDA platform, which enables parallel computing on GPUs, and can be performed entirely from a remote location. These jobs typically involve programming in C, C++, or Python, and require knowledge of parallel computing concepts. Remote CUDA roles are common in industries like AI, scientific computing, data analytics, and graphics rendering, allowing professionals to collaborate with teams globally without needing to relocate.

How much do CUDA engineers make?

CUDA engineers typically earn between $80,000 and $150,000 annually, depending on experience, location, and industry. Senior roles or those with specialized skills in GPU programming and deep learning can command higher salaries, especially in tech hubs or companies focusing on AI and high-performance computing.
More about Cuda Remote jobs
What cities are hiring for Cuda Remote jobs? Cities with the most Cuda Remote job openings:
What are the most commonly searched types of Cuda jobs? The most popular types of Cuda jobs are:
What states have the most Cuda Remote jobs? States with the most job openings for Cuda Remote jobs include:
What job categories do people searching Cuda Remote jobs look for? The top searched job categories for Cuda Remote jobs are:
Cuda Remote jobs near you
Infographic showing various Cuda Remote job openings in the United States as of July 2026, with employment types broken down into 50% Full Time, and 50% Contract. Highlights an 100% Remote job distribution, with an average salary of $81,860 per year, or $39.4 per hour.

CUDA Kernel Engineer (Remote US)

Pragmatike

San Francisco, CA • On-site, Remote

Full-time

Medical, Dental, Vision, Retirement

Re-posted 6 days ago

Job description

Location: Remote US
Start date: ASAP
Languages: English (required)
About the Role
Pragmatike is hiring on behalf of a fast-growing AI startup recognized as a Top 10 GenAI company by GTM Capital, founded by MIT CSAIL researchers.
We are searching for a CUDA Kernel Engineer who has hands-on experience developing and optimizing NVIDIA CUDA kernels from scratch. You will work on the GPU performance layer powering large-scale, high-throughput AI systems used by Fortune 500 customers.
This role is ideal for someone who deeply understands NVIDIA GPU architecture, memory hierarchy, warp-level execution, and profiling workflowsnot someone coming from generic hardware, FPGA, or non-NVIDIA compute backgrounds. You will directly influence the GPU efficiency, throughput, and scalability of mission-critical AI systems.
What Youll Do
  • Design, implement, and optimize custom CUDA kernels for NVIDIA GPUs, with a focus on maximizing occupancy, memory throughput, and warp efficiency.
  • Profile GPU workloads using tools such as Nsight Compute, Nsight Systems, nvprof, and CUDA-MEMCHECK.
  • Analyze and eliminate performance bottlenecks including warp divergence, uncoalesced memory access, register pressure, and PCIe transfer overhead.
  • Improve GPU memory pipelines (global, shared, L2, texture memory) and ensure proper memory coalescing.
  • Collaborate closely with AI systems, model acceleration, and backend distributed systems teams.
  • Contribute to GPU architecture decisions, kernel libraries, and internal performance-engineering best practices.

What Were Looking For
  • Proven track record building NVIDIA CUDA kernels from scratchnot just calling existing libraries.
  • Strong ability to optimize kernels (tiling strategies, occupancy tuning, shared memory design, warp scheduling).
  • Deep understanding of CUDA threads, warps, blocks, and grids, GPU memory hierarchy and memory coalescing, as well as warp divergence (how to detect, analyze, and mitigate it)
  • Experience diagnosing PCIe bottlenecks and optimizing host-device transfers (pinned memory, streams, batching, overlap).
  • Familiarity with C++, CUDA runtime APIs, and GPU debugging/profiling tooling.

Bonus Points
  • Experience with multi-GPU or distributed GPU systems (NCCL, NVLink, MIG).
  • Background in GPU acceleration for ML frameworks or HPC workloads.
  • Knowledge of model inference optimization (TensorRT, CUDA Graphs, CUTLASS).
  • Exposure to compiler-level optimization or PTX/SASS analysis.
  • Startup experience or comfort working in fast-moving, ambiguous environments.

Why This Role Will Pivot Your Career
  • Research pedigree: MIT CSAIL founders recognized for breakthrough AI and systems contributions.
  • Customer impact: Deploy AI solutions powering Fortune 500 clients.
  • Industry momentum: Lab alumni have led high-value acquisitions (MosaicML Databricks, Run:AI Nvidia, W&B CoreWeave).
  • Funding & growth: Oversubscribed seed round, next funding in 2026.
  • Career growth & influence: Lead AI initiatives, optimize pipelines, and directly impact production AI systems at scale.
  • Culture & autonomy: Own critical systems while collaborating with world-class engineers.
  • Aspirational impact: Solve GPU/AI performance challenges few engineers ever face.

Benefits
  • Competitive salary & equity options
  • Sign-on bonus
  • Health, Dental, and Vision
  • 401k

Pragmatike is an Equal Opportunity Employer and is committed to providing equal employment opportunities to all applicants without discrimination. We recruit on behalf of our clients and prohibit discrimination and harassment based on race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation, and training.We are committed to a fair and inclusive hiring process. We process your personal data solely for recruitment purposes, in accordance with applicable privacy laws, and maintain reasonable safeguards to protect your information. Your data may be shared with our client(s) for hiring consideration, but will not be disclosed to third parties outside of the recruitment process.

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