About RecogniRecogni’s mission is to design a vision-oriented artificial intelligence system from the ground up. The system will deliver unprecedented inference performance through novel edge processing, allowing vehicles to see farther and make driving decisions faster than humans while consuming minimal amounts of energy.Backed by GreatPoint Ventures, Toyota AI Ventures, BMW i Ventures and other leading VC partners, the company is headquartered in San Jose, California with additional operations in Munich, Germany.About the role
As a perception engineer at Recogni you will be building the AI-vision part of our actual product. From multi-modal 3D object detection, over panoptic segmentation all the way to kalman filter tracking - you will be part of the team that is building an end-to-end state-of-the-art autonomous drive perception stack, inspired but not limited by and out-performing today’s approaches to help our customers get to full autonomy within a commercially and technologically viable envelope.
Responsibilities
Qualifications
Why you might want to join the Recogni team
Recogni's culture was built on the following values that are equally important to us as business:
Recogni is an equal opportunity employer. We believe that a diverse team is better at tackling complex problems and coming up with innovative solutions. All qualified applicants will receive consideration for employment without regard to age, color, gender identity or expression, marital status, national origin, disability, protected veteran status, race, religion, pregnancy, sexual orientation, or any other characteristic protected by applicable laws, regulations and ordinances
The automobile industry has arrived at a crossroads. The transition to electric vehicles (EV) and the vitalized development of fully-autonomous vehicles (AV) has placed a big burden on fitting extraordinary amounts of computational power for artificial intelligence within the energy budget of batteries without affecting range. While battery technology is improving slowly, advances in compute efficiency have stalled as mere Moore's Law scaling of computational architectures from the past are nearly at an end