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. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the DL Kernel Library team and see how you can make a lasting impact on the world.
We're looking for Software Engineer Interns to help develop groundbreaking technologies in the deep learning inference and training systems software stack! We build innovative AI systems software, primarily cuDNN and FlashInfer. As a member of the team, you'll develop libraries, code generators, and GPU kernel technologies for NVIDIA's hardware architecture. This means designing and building things like new abstractions, efficient kernel implementations, and kernel code generators to accelerate large language models, agents, and other high-impact AI workloads.
What you'll be doing:
- Innovating and developing new AI systems technologies for efficient inference and training
- Designing, implementing, and optimizing kernels for high impact AI workloads
- Designing and implementing extensible abstractions for serving engines and training frameworks
- Building efficient just-in-time domain specific compilers and runtimes
- Collaborating closely with other engineers at NVIDIA across deep learning frameworks, libraries, kernels, and GPU arch teams
- Contributing to open source communities across FlashInfer, cuDNN, vLLM, SGLang, PyTorch, JAX, and XLA
What we need to see:
- BS, MS, or PhD degree in Computer Science, Electrical Engineering, or related field (or equivalent experience)
- Strong experience in developing or using deep learning frameworks (e.g. PyTorch, JAX, TensorFlow, ONNX, etc) or inference engines and runtimes such as vLLM, SGLang, and MLC.
- Strong Python and C/C++ programming skills
Ways to stand out from the crowd:
- Background in domain specific compiler and library solutions for LLM inference and training (e.g. FlashInfer, Flash Attention)
- Expertise in training frameworks and inference engines
- Expertise in machine learning compilers (e.g. Apache TVM, MLIR)
- Strong experience in GPU kernel development and performance optimizations (especially using CUDA C/C++, CUTLASS DSL, cuTile, Triton, or similar)
- Open source project ownership or contributions
NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you!.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 108,000 USD - 178,250 USD for Level 1, and 124,000 USD - 195,500 USD for Level 2.You will also be eligible for equity and benefits.
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.Skills Required
- BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field, or equivalent experience
- Strong experience developing or using deep learning frameworks such as PyTorch, JAX, TensorFlow, or ONNX
- Strong Python programming skills
- Strong C or C++ programming skills
- Background in domain-specific compiler and library solutions for LLM inference and training
- Expertise in training frameworks and inference engines
- Expertise in machine learning compilers such as Apache TVM or MLIR
- Experience in GPU kernel development and performance optimization using CUDA C/C++, CUTLASS DSL, cuTile, Triton, or similar technologies
- Open-source project ownership or contributions
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
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Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
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Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
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Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
