Senior AI/ML Perception Engineer

Key Responsibilities:

• 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.
• Contribute to the development of robust motion planning and navigation systems.
• Participate in real-time systems design and optimization to meet the high-performance requirements of autonomous driving.
• 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.

Required Skills:

• 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
• Strong 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.