Autonomous Driving Vehicle Perception Engineer

• Design and implement advanced perception algorithms for autonomous vehicles using LiDAR cameras radar and GNSS.
• Develop and optimize sensor fusion techniques to combine data from multiple sensors improving the accuracy and reliability of perception systems.
• Create algorithms for object detection tracking semantic segmentation and classification from 3D point clouds (LiDAR) and camera data.
• Work on Simultaneous Localization and Mapping (SLAM) algorithms including Graph SLAM LIO-SAM and visual-inertial SLAM.
• Develop sensor calibration techniques (intrinsic and extrinsic) and coordinate transformations between sensors.
• Participate in real-time systems design and optimization to meet the high-performance requirements of autonomous driving.
• Work with ROS2 for integration and deployment of perception algorithms.
• Develop test and deploy machine learning models for perception tasks such as object detection and segmentation.
• Collaborate with cross-functional teams including software engineers data scientists and hardware teams to deliver end-to-end solutions.
• Stay up-to-date with industry trends and emerging technologies to innovate and improve perception systems.

• Minimum 3 years of experience in sensor calibration multi-sensor fusion or related domains.
• Strong foundation in linear algebra 3D geometry coordinate frames quaternions probability Bayesian filtering and data association.
• Hands-on experience with intrinsic and extrinsic calibration of LiDAR cameras and radar including geometric calibration coordinate transforms and sensor synchronization.
• Proven experience with perception algorithms for autonomous systems particularly in the areas of LiDAR camera radar GNSS or other sensor modalities.
• Deep understanding of LiDAR technology point cloud data structures and processing techniques; experience with PCL or Open3D.
• Proficiency in sensor fusion for combining data from LiDAR camera radar and GNSS including handling time synchronization and motion distortion.
• Solid background in computer vision techniques; experience with OpenCV and object detection models such as YOLO Faster R-CNN or SSD.
• Experience with deep learning frameworks (TensorFlow or PyTorch) for object detection and segmentation tasks.
• Hands-on experience with multi-object tracking algorithms such as SORT DeepSORT Kalman Filters UKF IMM or JPDA.
• Strong programming skills in C and Python; familiarity with geometric optimization libraries.
• Familiarity with ROS2 for perception-based autonomous systems development.
• Experience with parallel computing for real-time performance optimization (e.g. CUDA OpenCL).

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