Computer Vision Perception Engineer (Autonomous Driving)

VDart, Inc.
Detroit, United States of America
17 days ago

Role details

Contract type
Temporary contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Intermediate

Job location

Detroit, United States of America

Tech stack

Algorithm Design
Artificial Neural Networks
Computer Vision
C++
Computer Programming
Python
Object Detection
OpenCV
TensorFlow
Sensor Fusion
Spatial Data Infrastructures
PyTorch
Deep Learning
Convolutional Neural Networks
Feature Extraction
Lidar
Data Pipelines

Job description

  • 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.

Requirements

  • 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.).

About the company

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