Intelligent machines powered by AI computers that can learn, reason and interact with people are no longer science fiction. Today, a self-driving car powered by AI can meander through a country road at night and find its way. An AI-powered robot can learn motor skills through trial and error. This is truly an extraordinary time. The era of AI has begun.
Image recognition and speech recognition — GPU deep learning has provided the foundation for machines to learn, perceive, reason and solve problems. The GPU started out as the engine for simulating human imagination, conjuring up the amazing virtual worlds of video games and Hollywood films. Now, NVIDIA’s GPU runs deep learning algorithms, simulating human intelligence, and acts as the brain of computers, robots and self-driving cars that can perceive and understand the world. Just as human imagination and intelligence are linked, computer graphics and artificial intelligence come together in our architecture. Two modes of the human brain, two modes of the GPU. This may explain why NVIDIA GPUs are used broadly for deep learning, and NVIDIA is increasingly known as “the AI computing company.”
Come, join our DL Architecture team and help build the real-time, cost-effective computing platform driving our success in this exciting and quickly growing field.
What you'll be doing:
- 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.
What we need to see:
- An MS or PhD degree in computer science, computer architecture, electrical engineering or related field. A strong background in several of the relevant areas listed below can substitute for an advanced degree.
- 3+ years of relevant work experience in at least a few of the following relevant areas is required:
- Machine learning (with focus on Deep Neural Networks), including a solid understanding of DL fundamentals; Experience adapting and training DNNs for various tasks; Experience developing code for one or more of the DNN training frameworks (such as Caffe, TensorFlow or Torch)
- Numerical analysis
- Performance analysis and optimization
- Computer architecture
- Programming fluency with C++ and ideally Python
- A familiarity with GPU computing (CUDA, OpenCL, OpenACC) and HPC (MPI, OpenMP) is a plus
NVIDIA is a diverse and equal opportunity employer