We are looking for extraordinary Deep Learning Software Engineers to develop and productize NVIDIA's deep learning solutions in autonomous driving vehicles. As a member of our Solution Engineering-Automotive Machine Learning team, you will utilize ground breaking NVIDIA deep learning model training/inference software libraries for deployment on NVIDIA's hardware architecture. You will develop new deep learning architectures, train deep learning models in low precision, and compile and optimize DNN graphs. As a part of this role, you will be building a close technical relationship with our automotive partners during product development and coordinate with the architecture and software teams to develop the best solution for partners working on our platforms.
What you'll be doing:
- Train, fine-tune, optimize and customize perception DNNs in low precision (FP16/INT8)
- Apply low precision inference, quantization, and compression 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 ideas to improve NVIDIA's automotive DNNs.
What we need to see:
- MS or PhD degree in computer science, computer vision, computer architecture or related technical field
- 6+ years of work experience in software development
- 2+ years of experience in developing or using deep learning frameworks (e.g. TensorFlow, Keras, PyTorch, Caffe, 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
- Solid technical foundation in CPU and GPU architectures, containers (nvidia-docker), numeric libraries, modular software design
- Familiar with state-of-the-art CNN/LSTM architectures
- 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 complex projects.
Ways to stand out from the crowd:
- Experience with low precision inference, quantization, compression of DNNs
- Experience with NVIDIA software libraries such as CUDA and TensorRT
- Open source project ownership or contribution, healthy GitHub repositories, guiding and/or mentoring experience.