Description
We are now looking for a Deep Learning Computer Architect to help design hardware accelerator and processor architectures that enable state-of-the-art machine learning and data analytics algorithms and applications on our next-generation mobile, embedded, and datacenter platforms.
As a member of our deep learning architecture team, you will contribute to features that help next-generation GPUs advance the state of AI. This position requires you to keep up with the latest DL research and collaborate with diverse teams (internal and external to NVIDIA), including DL researchers, hardware architects, and software engineers.
Your day-to-day work will include analyzing the behavior of various deep learning methods, proposing new features to accelerate or enable various methods, and studying the benefits of the proposed features.
To be successful in this role, you will need to have a strong background in computer science, computer architecture, electrical engineering, or a related field, and experience in at least a few of the following relevant areas:
- Computer architecture, including GPU and system-level architecture
- Performance analysis and optimization
- Experience with LLM workloads, including performance tuning considerations such as parallelization and fusion strategies
- Experience with core deep learning kernels such as matrix multiply, attention, and convolution
You will also need to have programming fluency with C++ and ideally Python, experience with GPU computing (CUDA), and experience with deep learning frameworks like PyTorch.
You will also be eligible for equity and benefits.