Purpose of the Role:
As a Senior Computer Vision Engineer, you will be part of a team working towards NIO’s autonomous vehicle vision. You will be working with bright, passionate people to implement the next generation automotive vision and machine learning algorithms.
The Advanced Technologies and Autonomy team is responsible for delivering highly available, high quality systems to enable NIO’s Autonomous driving vehicles. Our mission is to provide the next generation of hardware, software and algorithmic solutions. This includes but not limited to sensing, compute, storage as well as vehicle controls and safety system compute.
What the team works on:
• Autonomy hardware and software architecture
• Design, development, integration, and test of autonomous compute and sensing hardware
• Mass storage and Event Data Recorders
• Vehicle and Safety Controller HW and related functions
• Environment and Sensor modeling and simulation
• Autonomy Al and Controls
• Autonomy R&D Tools
• Autonomy compute and sensing HW and SW redundancy
• Sensing, GPS and IMU hardware, software, and integration
• Autonomy compute communication (sensing, compute, and controller inter-ECU communication)
· Expert knowledge of object detection and classification, with a focus on machine learning and deep learning
· Image labeling and annotation for training
· Vision aided navigation
· Experience with Simultaneous Localization and Mapping (SLAM) algorithms.
· Camera calibration techniques, online and offline
· Deep understanding of automotive camera sensor technologies, interfaces, data formats
· Object tracking and data association, sensor fusion, estimation, scene recognition
· Generation and tracking of key performance indicators and regression testing
· Skilled knowledge with parallel programming in CUDA or OpenCL
· Development or use of simulation environments
· Experience working on robotic and/or automotive electronics hardware is a plus
· 2+ years of experience developing software for shipping products
· Strong C/C++ software development experience
· Experience using issue tracking (JIRA) and source control (git) tools
· Previous experience in developing automotive vision a plus (lane tracking, vehicle detection and tracking, traffic light/sign detection, ground plane estimation)
· Experience with existing ADAS technologies such as adaptive cruise control, automatic emergency braking, and lane keeping
· Understanding of ADAS sensors such as radar, camera, ultrasonic, and lidar, including the measurement and data-reduction, target identification and environmental synthesis, and sensor fusion
· Previous use or contributions to open source computer vision libraries, such as OpenCV
· Previous use of machine learning frameworks such as Caffe and TensorFlow.