Senior Perception Engineer - Autonomous Vehicles

NVIDIA•Us, CA

22h

About The Position

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. We are now looking for an extraordinary Senior Perception Engineer to develop and productize NVIDIA’s autonomous driving solutions. As a member of our perception team, you will be driving E2E solutions for various components of camera perception, online map generation and scene understanding tasks. You will be challenged to improve robustness and accuracy as well as efficiency of the solutions to fully enable autonomous driving anywhere and anytime.

Requirements

3+ years of work experience and technical leadership demonstrating high technical and organizational complexity in related academic or industrial fields.
8+ years of hands-on experience in developing deep learning and algorithms to solve sophisticated real world problems, and proficiency in using deep learning frameworks (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 teamwork skills as we work as a tightly-knit team, always discussing and learning from each other.
MS/PhD in CS, EE, sciences or related fields (or equivalent experience)

Responsibilities

Designing end2end solutions for Perception and AV stack to enable camera perception, online map generation and understanding tasks in complex driving environments. Example perception targets include road topology and geometry with key elements such as lane lines, road boundaries, wait lines, crosswalks, etc.
Applied research and development of innovative deep learning models to improve output accuracy of detection and classification solutions under challenging and diverse scenarios. Develop generalizable approaches to support ODD and Country/region expansion
Productize the developed 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, labeling efficiency optimization, so that value of data is maximized.