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Cuda Engineer information

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$36K

$105.9K

$135.7K

How much do cuda engineer jobs pay per year?

As of Aug 18, 2026, the average yearly pay for cuda engineer in California is $105,877.00, according to ZipRecruiter salary data. Most workers in this role earn between $87,300.00 and $134,200.00 per year, depending on experience, location, and employer.

What is a CUDA engineer?

CUDA Engineers are software developers who specialize in using NVIDIA's CUDA (Compute Unified Device Architecture) platform to write programs that run on Graphics Processing Units (GPUs). They optimize and accelerate computational tasks by parallelizing code, making use of GPUs’ capabilities for high-performance computing. CUDA Engineers often work in fields like machine learning, scientific computing, and graphics, where large amounts of data need to be processed quickly. Their expertise includes proficiency in C/C++, CUDA programming, and understanding GPU hardware and parallel computing concepts.

What are the key skills and qualifications needed to thrive as a CUDA engineer?

To thrive as a CUDA Engineer, you need a strong proficiency in C/C++ programming, parallel computing concepts, and deep knowledge of GPU architectures, often supported by a computer science or engineering degree. Experience with NVIDIA CUDA Toolkit, profiling/debugging tools, and sometimes certifications like NVIDIA DLI are highly valuable. Strong problem-solving, attention to detail, and effective communication skills help you optimize code and collaborate across teams. These skills ensure efficient development of high-performance GPU applications and successful project delivery in compute-intensive fields.

What are some common challenges faced by CUDA engineers when optimizing GPU-accelerated applications?

CUDA Engineers frequently encounter challenges such as managing memory effectively between the host and the device, optimizing kernel performance, and minimizing data transfer bottlenecks. Debugging parallel code can also be complex due to race conditions and the difficulty of reproducing timing-related bugs. Collaborating closely with software developers and data scientists is essential to ensure that GPU resources are leveraged efficiently and that the application's overall performance meets project goals.

What is the difference between Cuda Engineer vs GPU Developer?

AspectCuda EngineerGPU Developer
Required CredentialsBachelor's or Master's in Computer Science, Engineering, or related; knowledge of CUDA, C++, parallel programmingBachelor's or Master's in Computer Science, Engineering, or related; experience with GPU programming, CUDA, OpenCL
Work EnvironmentResearch labs, tech companies, hardware firms focusing on GPU accelerationSoftware development teams, gaming, AI, scientific computing sectors
Employer & Industry UsageHardware manufacturers, AI companies, high-performance computing firmsGame development, scientific research, machine learning applications

While both roles involve GPU programming and CUDA expertise, a Cuda Engineer primarily focuses on developing and optimizing CUDA-based solutions for hardware acceleration. In contrast, a GPU Developer works on broader GPU programming tasks, including application development across various platforms. The roles often overlap but differ in scope and specific focus areas.

What are popular job titles related to Cuda Engineer jobs in California?

For Cuda Engineer jobs in California, the most frequently searched job titles are:

What job categories do people searching Cuda Engineer jobs in California look for?

The top searched job categories for Cuda Engineer jobs in California are:

What cities in California are hiring for Cuda Engineer jobs?

Cities in California with the most Cuda Engineer job openings:

Cuda Engineer jobs near you
Infographic showing various Cuda Engineer job openings in California as of August 2026, with employment types broken down into 95% Full Time, 2% Part Time, and 3% Contract. Highlights an 85% Physical, 6% Hybrid, and 9% Remote job distribution, with an average salary of $105,877 per year, or $50.9 per hour.

Senior Software Engineer, CUDA Core Libraries

Segment (Twilio)

Santa Clara, CA • On-site

$184 - $287.50/hr

Other

This job post has expired today. Applications are no longer accepted.

Job description

NVIDIA’s accelerated computing platform is the foundation of modern HPC and AI.At the core of this platform are the CUDA Core Libraries. C++ and Python libraries that enable developers to write fast, reliable, and scalable GPU-accelerated software! We are hiring a full-time Software Engineer to work on the CUDA Core Libraries that power GPU computing for both C++ and Python developers. This includes projects such asCCCL (Thrust, CUB, libcudacxx),cuda-python, andnumba-cuda. You will join the team building the foundational libraries, algorithms, and language/runtime infrastructure that make CUDA a speed-of-light experience for developers across deep learning, scientific computing, and data analytics!

What you’ll be doing:

  • Develop and implement CUDA Core Libraries inC++ and/or Python, including parallel algorithms and idiomatic language bindings for core CUDA functionality.

  • Compose, optimize, and evolve GPU algorithms and APIs, from high-level interfaces down to low-level performance tuning involving memory, parallelism, and synchronization.

  • Own features end-to-end: develop, implementation, testing, benchmarking, documentation, and long‑term maintenance.

  • Improve developer experience across the stack: CI, tests, benchmarks, packaging, examples, and docs.

  • Collaborate with senior CUDA engineers in design reviews, code reviews, and open‑source‑style workflows.

  • Engage with real users through issues, performance investigations, and API feedback.

What we need to see:

  • BS, MS, or PhD in Computer Science, Computer Engineering, or a related fieldor equivalent experience.

  • Minimum of 8+ years of related development experience

  • Strong programming skills inC++, Python, or both, with proven interest in systems‑level software (performance, memory, concurrency, API design).

  • Solid understanding of modern C++ (templates, generics, standard library) and/or Python library development and packaging.

  • Practical experience with parallel or heterogeneous programming(CUDA, OpenMP, GPU‑accelerated Python, or similar).

  • Experience contributing to production software or open‑source libraries, including testing, profiling, and code review.

  • Ability to work independently, scope problems, and drive projects to completion.

  • Clear written communication for technical design and documentation.

  • Comfort navigating large, multi‑language codebases (C++, Python, CMake, Pixi, CI systems).

Ways to stand out from the crowd:

  • Strong understanding of CPU/GPU architecture and how hardware details affect performance.

  • Hands‑on experience with CUDA C++, CUDA Python, PyTorch, JAX, Numba, CuPy, or similar GPU‑accelerated stacks.

  • Familiarity with Thrust, CUB, libcudacxx, or other modern C++/GPU libraries.

  • Experience with compiler infrastructure or tooling (LLVM, Clang tooling, MLIR).

  • Demonstrated interest in developer tools, library design, and making other developers faster.

If you care deeply about performance, enjoy working at the C++/Python boundary, and want to shape the core CUDA libraries relied on by thousands of developers, this role is a direct fit.

Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 4, and 224,000 USD - 356,500 USD for Level 5.

You will also be eligible for equity and benefits.

Applications for this job will be accepted at least until June 5, 2026.

This posting is for an existing vacancy.

NVIDIA uses AI tools in its recruiting processes.

NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.

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