NVIDIA is looking to develop and productize deep learning solutions in autonomous driving vehicles.
Requirements
- 2+ years of experience in developing or using deep learning frameworks (e.g. PyTorch, JAX, TensorFlow, ONNX, etc.)
- Experience with solving a computer vision task using deep neural networks, such as object detection, scene parsing, image segmentation.
- Strong Python and/or C/C++ programming skills
- Proven technical foundation in CPU and GPU architectures, containers (nvidia-docker), numeric libraries, modular software design
- Familiar with CNNs and Transformer architectures
- Experience with low precision inference, quantization, compression of DNNs
- Experience with NVIDIA software libraries such as CUDA and TensorRT
Responsibilities
- Train, fine-tune, optimize and customize perception DNNs in low precision (FP16/INT8)
- Apply sophisticated quantization of DNNs
- Improve DNN architectures using ML algorithms on NVIDIA GPUs or DLAs
- Continuously improve inference speed, accuracy and power consumption of DNNs
- Stay up to date with the latest research and innovations in deep learning, implement and experiment with new insights to improve NVIDIA's automotive DNNs.
- develop new deep learning architectures, train deep learning models, and compile and optimize DNN graphs
- apply ground breaking NVIDIA deep learning model training/inference software libraries for deployment on NVIDIA's hardware architecture
Other
- MS or PhD degree in computer science, computer vision, computer architecture or equivalent experience in technical field
- 5+ years of work experience in software development.
- Willing to take action and have strong analytical skills.
- Strong time-management and organization skills for coordinating multiple initiatives, priorities and implementations of new technology and products into very sophisticated projects.
- Open source project ownership or contribution, healthy GitHub repositories, guiding and/or mentoring experience
