Description

You design and implement perception algorithms for autonomous vehicles using LiDAR, cameras, radar, and GNSS. You own sensor fusion, object detection, tracking, and semantic segmentation to ensure reliable performance in adverse weather.

Responsibilities

• Develop and optimize multi-sensor fusion techniques to improve perception accuracy and reliability.
• Create algorithms for object detection, tracking, and classification from 3D point clouds and camera data.
• Implement sensor calibration techniques and coordinate transformations between LiDAR, cameras, and radar.
• Build robust perception systems that maintain performance in rain, snow, fog, and low-light conditions.
• Deploy machine learning models and integrate perception algorithms into larger autonomous systems using ROS2.

Required Skills

• 3+ years of experience in sensor calibration, multi-sensor fusion, or perception domains.
• Strong proficiency in C++ and Python for real-time systems development.
• Deep understanding of LiDAR technology, point cloud data structures, and processing techniques (PCL or Open3D).
• Hands-on experience with computer vision techniques (OpenCV) and object detection models (YOLO, Faster R-CNN, SSD).
• Proficiency in deep learning frameworks (TensorFlow or PyTorch) for detection and tracking tasks.
• Experience with multi-object tracking algorithms (SORT, DeepSORT, Kalman Filters, UKF, IMM, JPDA).
• Solid foundation in linear algebra, 3D geometry, quaternions, probability, and Bayesian filtering.
• Experience with ROS2 for integration and deployment of perception algorithms.
• Familiarity with parallel computing for real-time optimization (CUDA, OpenCL).

Key Skills

Education

Bachelor's degree