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.
Requirements
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.
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
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.
Benefits
equity
benefits
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Hard skills
deep learning frameworksPyTorchJAXTensorFlowONNXPythonC/C++DNN architecturesquantizationcomputer vision