System Software Engineer - Deep Learning

About NVIDIA

NVIDIA has been transforming computer graphics, PC gaming, and accelerated computing for more than 25 years. It’s a unique legacy of innovation that’s fueled by great technology—and amazing people. Today, we’re tapping into the unlimited potential of AI to define the next era of computing. An era in which our GPU acts as the brains of computers, robots, and self-driving cars that can understand the world. Doing what’s never been done before takes vision, innovation, and the world’s best talent. As an NVIDIAN, you’ll be immersed in a diverse, supportive environment where everyone is inspired to do their best work. Come join the team and see how you can make a lasting impact on the world.

NVIDIA DRIVE® AI platform supports autonomous driving, in-cabin functions, and driver monitoring, plus other safety features—all in a compact, energy-efficient package. As a part of the Tegra Solutions Engineering Business Unit, we need passionate, hard-working and creative people to help us tackle more of these challenging opportunities in Autonomous Driving and In-Car Infotainment.

What you'll be doing:

• Develop solutions around NVIDIA GPU and Deep learning accelerators (DLA) to accelerate inference ADAS Systems
• Develop SDKs / Frameworks to accelerate LLMs and state of art models for NVIDIA Drive Platform
• Conduct benchmarking and evaluation activities to continuously improve inference latency, accuracy and power consumption of the models
• Stay up to date with the latest research and innovations in deep learning, implement and experiment with new ideas to improve NVIDIA's automotive DNNs
• Responsible for the technical relationship and assisting the automotive customer in building creative solutions based on NVIDIA technology
• Collaborate with engineering teams in our US, APAC, India and Europe locations

What we need to see:

• BS or MS degree in Computer Science, Computer Engineering or Electrical Engineering
• 5+ Years of Experience in developing or using deep learning frameworks (e.g. TensorFlow, Keras, PyTorch, Caffe, ONNX, etc.)
• Proven experience in optimizing DNN Layers for GPU or other DSPs
• Understanding of compilers infrastructure like LLVM and MLIR and associated flow of optimization for DL accelerators
• Proficiency in C and C++ and Data Structures
• Strong OS fundamentals and knowledge of CPU/GPU architecture
• Familiar with state-of-the-art CNN/LSTM/Transformers architecture