Intelligent machines powered by Artificial Intelligence computers that can learn, reason and interact with people are no longer science fiction. GPU Deep Learning has provided the foundation for machines to learn, perceive, reason and solve problems. 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.We are now looking for an extraordinary Perception Engineer at all levels to develop and productize NVIDIA's autonomous driving solutions. As a member of our perception team, you will work on building world-class 3D obstacle perception solutions based on multiple input modalities, such as cameras, radars, and ultrasonics. The primary approach will be deep learning. You will be challenged to improve robustness and accuracy as well as efficiency of the solutions to fully enable autonomous driving anywhere and anytime.What You'll Be Doing:Perception development with application focus on object detection and tracking for highway driving, urban driving, and parking. Types of objects will include dynamic/static objects and parking spots and nearby structures.Applied research and develop innovative deep learning and sensor fusion algorithms to improve output accuracy of 3D obstacle perception solutions under challenging and diverse scenarios.Identify and analyze the strength and weakness of the developed 3D obstacle perception solutions using large scale benchmark data (both real and synthetic) and improve them iteratively through KPI building and optimization.Productize the developed 3D obstacle perception solutions by meeting product requirements for safety, latency, and SW robustness.Drive and prioritize data-driven development by working with large data collection and labeling teams to bring in high value data to improve perception system accuracy. Efforts will include data collection prioritization and planning, labeling prioritization, so that value of data is maximized.What We Need To See:BS/MS/PhD in CS, EE, science or related fields (or equivalent experience)3+ years of hands-on work experience in developing deep learning and algorithms to solve complex real world problems, and proficiency in using deep learning framework (e.g., PyTorch).Experience in data-driven development and collaboration with data and ground truth teams.Strong programming skills in python and/or C++.Outstanding communication and collaboration skills as we work as a tightly-knit team, always discussing and learning from each other.Ways To Stand Out From The Crowd:Proven expertise in developing perception solutions for autonomous driving or robotics using deep learning and experience on diverse sensor modalities such as cameras, ultrasonics, and radar.Hands-on experience in developing and deploying DNN-based solutions to embedded platforms for real time applications.Proven expertise in deep learning backed up by technical publications in leading conferences/journals.Good understanding of fundamentals of 3D computer vision, camera calibrations including intrinsic and extrinsic. Background in algorithm design for object tracking.Experience with development in CUDA language. The ability to implement CUDA kernels as part of training or inference pipelines.
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