Perception AI/ML Engineer

Job Requirements Key Responsibilities: 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. Contribute to the development of robust motion planning and navigation systems. Work with software stacks like ROS2 (Robot Operating System 2) for integration and deployment of perception algorithms. Develop, test, and deploy machine learning models for perception tasks (e.g., object detection, 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, research papers, and emerging technologies to innovate and improve perception systems. Work Experience Required Skills: Proven experience with perception algorithms for autonomous systems, particularly in the areas of LiDAR, camera, radar, GNSS, or other sensor modalities. Understanding of LiDAR technology, point cloud data structures, and processing techniques Proficiency in programming languages such as C/C++, Python, or similar. In-depth knowledge of sensor fusion techniques (Kalman Filters, Extended Kalman Filters, Unscented Kalman Filters, Particle Filters) for combining data from LiDAR, camera, radar, and GNSS. Solid background in computer vision techniques (e.g., object detection, semantic segmentation, feature extraction). Experience in deep learning frameworks such as TensorFlow or PyTorch for object detection and segmentation tasks. Knowledge of SLAM (Simultaneous Localization and Mapping) and localization algorithms, including GraphSLAM, LIO-SAM, GTSAM, ORB-SLAM, and related technologies. Familiarity with ROS2 for the development of perception-based robotic systems and autonomous vehicles. Experience with multi-object tracking algorithms such as DeepSORT, SORT, and Kalman Filter-based tracking. Strong understanding of real-time systems and optimizing for low-latency processing. Proficiency in sensor calibration techniques and algorithms for both intrinsic and extrinsic calibration of LiDAR, cameras, radar, and GNSS. Hands-on experience with PCL (Point Cloud Library) and OpenCV for 3D point cloud and image processing. Experience with parallel computing and optimizing algorithms for real-time performance (e.g., CUDA, OpenCL). Experience with object detection models such as YOLO , Faster R-CNN , SSD , or similar. Show more Show less