ROLE: Computer Vision Perception Engineer (Autonomous Driving)
📌Location: Detroit, MI, USA.
✅Pay Rate: $75/Hour
✅Eligibility: US Citizen & Green Card holder, GCAD, L2 EAD & H4 EAD
✅Work Type: On-site (W2 )
✅Employment Type: Contract
✅Joining Date: ASAP
✅Experience: At least 5 years of experience in Computer Vision Perception Engineer - Autonomous Driving, Strong expertise in computer vision and deep learning for object detection and segmentation tasks.
✅Job Description:
What You Will Do:
· Design and implement computer vision algorithms for object detection and segmentation using camera and LiDAR data fusion.
· Develop deep learning models for 2D and 3D object detection, including implementation and optimization of YOLO, Faster R-CNN, SSD, and transformer-based architectures.
· Create and optimize LiDAR point cloud processing pipelines using PCL and Open3D for 3D object detection and segmentation.
· Implement sensor fusion techniques to combine camera and LiDAR data for enhanced object detection accuracy.
· Develop instance and semantic segmentation algorithms using state-of-the-art models like Mask R-CNN, U-Net, and DeepLab.
· Implement and optimize deep learning models specifically designed for LiDAR point clouds, including PointNet, PointNet++, and other 3D neural network architectures.
· Develop robust perception algorithms that maintain performance in adverse weather conditions such as rain, snow, fog, and low-light scenarios.
· Build and maintain computer vision pipelines using OpenCV for image preprocessing, feature extraction, and geometric transformations.
· Design and implement multi-object tracking systems using Kalman filtering, SORT, and DeepSORT algorithms.
· Work with ROS2 for integration and deployment of perception algorithms.
· Optimize deep learning models for edge deployment and real-time inference performance.
· Develop robust evaluation metrics and testing frameworks for object detection systems.
· Collaborate with cross-functional teams to integrate perception algorithms into larger autonomous systems.
· Stay up-to-date with industry trends and emerging technologies to innovate and improve perception systems.
✅What You Will Bring:
· Strong expertise in computer vision and deep learning for object detection and segmentation tasks.
· Proficiency in deep learning frameworks (PyTorch and TensorFlow) with hands-on experience implementing detection models (YOLO, Faster R-CNN, SSD, RetinaNet, Detectron, etc.).
· Extensive experience with OpenCV for image processing and computer vision applications.
· Solid background in 3D perception using LiDAR point clouds; proficiency with PCL and Open3D libraries.
· Familiarity with LiDAR-specific deep learning models such as PointNet, PointNet++, VoxelNet, and other point cloud neural network architectures.
· Experience in developing and improving perception models for adverse weather conditions (rain, snow, fog) including domain adaptation and robust feature extraction techniques.
· Experience with sensor fusion techniques for combining camera and LiDAR data streams.
· Strong programming skills in Python and C++ for algorithm development and optimization.
· Experience with model optimization techniques for real-time inference.
· Familiarity with 3D geometry, coordinate transformations, and spatial data processing.
· Knowledge of evaluation metrics for object detection and tracking systems (mAP, IoU, custom metrics, etc.).
