Senior Deep Learning Systems Architect
$224k - $357k • Remote • US, CA, Santa Clara
Posted 1d ago
Job Location
US, CA, Santa Clara
Tech Stack
Remote Work Policy
Fully remote
Categories
Machine Learning Engineer
About the job
NVIDIA is seeking architects to design hardware accelerator and processor architectures that enable state-of-the-art machine learning and data analytics algorithms and applications on next-generation mobile, embedded, and datacenter platforms. This role offers the opportunity to make a real impact in a dynamic, technology-focused company. As a member of the deep learning architecture team, you will contribute to features that advance the state of AI on next-generation GPUs and systems. This position requires staying current with the latest DL research and collaborating with diverse teams, including DL researchers, hardware architects, and software engineers.
Responsibilities
- Contribute to features that advance next-generation GPUs and systems for AI.
- Collaborate with internal and external teams, including DL researchers, hardware architects, and software engineers.
- Participate in engineering projects and co-design system architecture from conception to prototyping.
- Analyze AI/DL workloads and their mapping to hardware and systems.
- Identify potential improvements and bottlenecks, proposing solutions to accelerate and enhance current systems/methods.
- Perform comprehensive analyses of deep learning techniques and system optimizations.
- Build analytical models and implement prototypes to test and prove ideas.
Requirements
- MS or PhD degree in computer science, computer architecture, electrical engineering, or related field, with 10+ years of relevant work experience, or equivalent experience.
- Strong background in Machine Learning (focus on Deep Neural Networks), including DL fundamentals.
- Experience adapting and training DNNs for various tasks.
- Experience developing code for DNN training frameworks like PyTorch, TensorFlow, or JAX.
- Proficiency in numerical analysis, performance analysis and optimization, and computer architecture.
- Programming fluency in C++ and Python.
- Experience with GPU computing (CUDA, OpenCL, OpenACC) and HPC (MPI, OpenMP) is a significant advantage.
Benefits
- Equity
- Benefits