Autonomous Driving Vehicle Perception Engineer

What You will Do:

• 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.

What You Will Bring:

• 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).