Waymo, an autonomous driving technology company, is seeking a Software Engineer specializing in Computer Vision and Deep Learning to join their Perception Team. This role is crucial in building the system that "sees" the world around Waymo's self-driving cars. As part of this team, you'll conduct novel research to address real-world perception problems and collaborate with research teams at Alphabet.
At Waymo, you'll have access to millions of miles of driving data from diverse sensors, enabling you to develop complex models and techniques at scale. Your improvements will immediately advance Waymo's large fleet of autonomous vehicles. This position offers a unique opportunity to apply machine learning to solve critical problems in autonomous driving, working with multi-modal, multi-task sensor fusion architectures.
Key responsibilities include developing object detection and tracking systems, segmentation algorithms, road understanding models, flow estimation, and future prediction capabilities. You'll also be involved in designing and developing large-scale foundation models trained on Waymo's vast data sets. The role requires creating data mining, labeling, training, and evaluation pipelines to enhance the Waymo Driver.
The ideal candidate should have a strong background in Computer Science with a focus on Computer Vision and Machine Learning. Experience with Python is essential, and knowledge of C++ is preferred. Waymo values innovation and research, so publications in top-tier conferences like CVPR, ICCV, or NeurIPS are a plus.
Waymo offers a competitive salary range of $158,000 to $200,000 USD, along with excellent benefits including comprehensive health insurance, mental wellness support, and financial perks such as equity and bonus opportunities. The company promotes work-life balance with flexible work arrangements and generous time off policies.
Join Waymo and be at the forefront of autonomous driving technology, working on projects that have the potential to revolutionize transportation and improve road safety on a global scale.