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Cuda Kernel Engineer Jobs (NOW HIRING)

Nvidia

OR · Hybrid

$122K - $161K/yr

Contribute to CUDA kernel and operator development for critical transformer components such as ... Proficient programming ability with modern C++ (C++11/14/17 and beyond). * Familiarity with popular ...

Nvidia

Senior Software Engineer - TensorRT Edge-LLM

Austin, TX · Hybrid

$121K - $160K/yr

Contribute to CUDA kernel and operator development for critical transformer components such as ... Proficient programming ability with modern C++ (C++11/14/17 and beyond). * Familiarity with popular ...

Baseten

Software Engineer - GPU Kernels

$143K/yr

... GPU Kernel Engineer to enhance AI model performance. This role focuses on designing high ... CUDA C++ API, Memory access patterns and bandwidth optimization, Numerical precision and ...

Baseten

Software Engineer - GPU Kernels

New York, NY · On-site

$180K - $360K/yr

THE ROLE We're seeking a GPU Kernel Engineer to join our team at the cutting edge of AI ... Write and optimize code using CUDA, PTX assembly, and architecture-specific techniques * Apply ...

Etched

Kernel Driver Software Engineer

San Jose, CA · On-site

$150K - $275K/yr

Design, develop, and maintain kernel-mode drivers ensuring high reliability, informative debug, and ... Experience with CUDA, OpenCL, or other GPU programming models. * Experience with performance ...

Etched

Kernel Driver Software Engineer

San Jose, CA · On-site

$150K - $275K/yr

Design, develop, and maintain kernel-mode drivers ensuring high reliability, informative debug, and ... Experience with CUDA, OpenCL, or other GPU programming models. * Experience with performance ...

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Cuda Kernel Engineer information

What are some common challenges faced by Cuda Kernel Engineers when optimizing GPU code for performance?

Cuda Kernel Engineers often encounter challenges such as managing memory hierarchy efficiently, minimizing data transfer between host and device, and avoiding thread divergence. Ensuring optimal occupancy and maximizing parallelism while preventing bottlenecks like bank conflicts or uncoalesced memory access are also key concerns. Collaborating closely with software architects and data scientists is common, as solutions frequently require balancing algorithmic accuracy with hardware limitations. Addressing these challenges requires continuous profiling, testing, and iterative optimization.

What are Cuda Kernel Engineers?

Cuda Kernel Engineers are specialized software developers who design, implement, and optimize parallel computing algorithms using NVIDIA's CUDA platform. They write 'kernels,' which are functions that run on Graphics Processing Units (GPUs) to accelerate computational tasks in areas such as machine learning, scientific simulations, and graphics rendering. These engineers need strong skills in C/C++ programming, GPU architecture, and performance optimization techniques. Their work is crucial for applications that require high-speed data processing and efficient resource utilization.

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

To thrive as a CUDA Kernel Engineer, you need strong proficiency in C/C++ programming, parallel computing concepts, and a solid foundation in GPU architectures, typically supported by a degree in computer science or a related field. Expertise in NVIDIA CUDA toolkits, GPU profiling tools like Nsight, and familiarity with version control systems are essential. Analytical thinking, problem-solving abilities, and effective collaboration skills help engineers optimize code and work well within development teams. These skills and qualities are crucial for delivering high-performance, scalable GPU solutions in computationally intensive applications.
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Infographic showing various Cuda Kernel Engineer job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 50% In-person, and 50% Remote job distribution.
Senior Software Engineer - TensorRT Edge-LLM

Senior Software Engineer - TensorRT Edge-LLM

Nvidia Corporation

Santa Clara, CA • On-site

$143K - $189K/yr

Full-time

Posted 27 days ago

Job description

Are you passionate about pushing the limits of real-time large language model inference? Join NVIDIA's TensorRT Edge-LLM team and help shape the next generation of edge AI for automotive and robotics. We build the software stack that enables Large Language, Vision-Language, and Multimodal (LLM/VLM/VLA) models to run efficiently on embedded and edge platforms - delivering cutting-edge generative AI experiences directly on-device.
What you'll be doing:
  • Develop and evolve a state-of-the-art inference framework in modern C++ that extends TensorRT with autoregressive model serving capabilities, including speculative decoding, LoRA, MoE, and KV cache management.
  • Design and implement compiler and runtime optimizations tailored for transformer-based models running on constrained, real-time platforms.
  • Collaborate with teams across CUDA, kernel libraries, compilers, and robotics to deliver high-performance, production-ready solutions.
  • Contribute to CUDA kernel and operator development for critical transformer components such as attention, GEMM, and MoE.
  • Benchmark, profile, and optimize inference performance across diverse embedded and automotive environments.
  • Stay ahead of the rapidly evolving LLM/VLM ecosystem and bring emerging techniques into product-grade software.

What we need to see:
  • BS, MS, PhD, or equivalent experience in Computer Science, Electrical/Computer Engineering, or a closely related field.
  • 4+ years of relevant software development experience.
  • Deep understanding of transformer models and inference optimization techniques (e.g., quantization, tensor parallelism, or memory-efficient scheduling).
  • Proficient programming ability with modern C++ (C++11/14/17 and beyond).
  • Familiarity with popular LLM frameworks and libraries such as TensorRT, TensorRT-LLM, vLLM, SGLang, MLC-LLM, or FlashInfer.
  • A track record of strong software design, execution, and collaboration across fields.

Ways to stand out from the crowd:
  • Demonstrated development experience or open-source contributions to LLM inference frameworks and libraries, such as SGLang, vLLM, or FlashInfer.
  • Proficiency with CUDA, including efficient kernel development, performance profiling, and GPU architecture fundamentals.
  • Prior work on autoregressive LLM serving systems, including speculative decoding or KV cache management.
  • Familiarity with compiler infrastructure for large language model inference.
  • Exposure to robotics or embedded AI pipelines, including optimizing for low-latency, resource-constrained systems.

NVIDIA is widely considered to be one of the technology world's most desirable employers. We hire some of the most brilliant and forward-thinking people in the world. If you thrive on innovation, autonomy, and technical excellence, come join us to shape the future of edge AI.
#LI-Hybrid
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD for Level 3, and 184,000 USD - 287,500 USD for Level 4.
You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until March 21, 2026.
This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering a diverse 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.
Nvidia logo

About Nvidia

Sourced by ZipRecruiter

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It's a unique legacy of innovation that's fueled by great technology--and amazing people. Today, we're tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what's never been done before takes vision, innovation, and the world's best talent.

Industry

Computer and electronic product manufacturing

Company size

10,000+ Employees

Headquarters location

Santa Clara, CA, US

Year founded

1993

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