Senior Performance Software Engineer, Deep Learning Libraries
At a glance
Senior Machine Learning Engineering role at NVIDIA. China, Shanghai · full-time.
Pay not stated Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build tuned GPU kernels for core deep learning work such as matrix multiplication, convolution and normalization
- Use regression tests and CI/CD practices to keep kernel changes reliable
- Work with compiler engineers to produce efficient assembly for GPU workloads
- Coordinate with training, inference and hardware teams to choose layers for tuning and adapt to new GPU features
What you bring
- A master's or PhD in computer science, computer engineering, applied math or a related field, or equivalent experience
- At least two years of relevant industry experience
- Strong C++ and software design skills, including debugging, performance measurement and test planning
- Experience with parallel programming and computer architecture, plus some assembly programming
Who this fits
This role suits a senior software engineer focused on low-level GPU performance, parallel algorithms and deep learning operations. It involves individual engineering work alongside compiler, training, inference and hardware teams.
From the employer
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