The place to find available career opportunities at NVIDIA for you and people you know. We are now looking for a Senior Performance Software Engineer for Deep Learning Libraries! Do you enjoy tuning parallel algorithms and analyzing their performance? If so, we want to hear from you! As a deep learning library performance software engineer, you will be developing optimized code to accelerate linear algebra and deep learning operations on NVIDIA GPUs. The team delivers high-performance code to NVIDIA’s cuDNN, cuBLAS, and TensorRT libraries to accelerate deep learning models. The team is proud to play an integral part in enabling the breakthroughs in domains such as image classification, speech recognition, and natural language processing. Join the team that is building the underlying software used across the world to power the revolution in artificial intelligence! We’re always striving for peak GPU efficiency on current and future-generation GPUs. To get a sense of the code we write, check out our CUTLASS open-source project showcasing performant matrix multiply on NVIDIA’s Tensor Cores with CUDA. This specific position primarily deals with code lower in the deep learning software stack, right down to the GPU HW.
What you'll be doing:
Writing highly tuned compute kernels to perform core deep learning operations (e.g. matrix multiplies, convolutions, normalizations)
Following general software engineering best practices including support for regression testing and CI/CD flows
Collaborating with teams across NVIDIA:
CUDA compiler team on generating optimal assembly code
Deep learning training and inference performance teams on which layers require optimization
Hardware and architecture teams on the programming model for new deep learning hardware features
What we need to see:
Masters or PhD degree or equivalent experience in Computer Science, Computer Engineering, Applied Math, or related field
2+ years of relevant industry experience
Demonstrated strong C++ programming and software design skills, including debugging, performance analysis, and test design
Experience with performance-oriented parallel programming, even if it’s not on GPUs (e.g. with OpenMP or pthreads)
Solid understanding of computer architecture and some experience with assembly programming
Identify bottlenecks, optimize resource utilization, and improve throughput.
Ways to stand out from the crowd:
Tuning BLAS or deep learning library kernel code
CUDA GPU programming
Numerical methods and linear algebra
LLVM, TVM tensor expressions, or TensorFlow MLIR
Skills Required
- Master’s or PhD degree, or equivalent experience, in Computer Science, Computer Engineering, Applied Math, or a related field
- 2+ years of relevant industry experience
- Strong C++ programming and software design skills
- Experience with debugging, performance analysis, and test design
- Experience with performance-oriented parallel programming, such as OpenMP or pthreads
- Solid understanding of computer architecture
- Some experience with assembly programming
- Experience identifying bottlenecks, optimizing resource utilization, and improving throughput
- Experience tuning BLAS or deep learning library kernel code
- CUDA GPU programming experience
- Knowledge of numerical methods and linear algebra
- Experience with LLVM, TVM tensor expressions, or TensorFlow MLIR
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.”
