Senior Software Engineer- Computer Vision

Responsibilities:

• Advanced Algorithm Development: Lead the design, optimization, and implementation of cutting-edge computer vision and signal processing algorithms for autonomous driving perception systems.
• Real-world Evaluation & Optimization: Work closely with sensor hardware teams to evaluate measurements from real-world traffic data, identify algorithmic deviations, and continuously improve algorithm performance.
• Cross-team Collaboration: Act as a key liaison between the development team and other departments to ensure that new ideas and solutions are successfully transitioned into production-level software and hardware.
• Leadership in Research: Drive research and development efforts for robotics perception software products, with a focus on classification, segmentation, detection, tracking, and prediction algorithms.
• Pipeline Design: Lead the design and implementation of a high-performance and robust 3D perception pipeline, ensuring seamless integration between LiDAR, camera, and other sensor data sources.

Qualifications:

• Education: M.S. or Ph.D. in Computer Science, Robotics, Electrical Engineering, or related fields.
• Experience: 5+ years of industry experience in developing computer vision and robotics perception algorithms.
• Programming Expertise: Advanced proficiency in Modern C++ (multi-threaded programming, software design patterns).
• Deep Understanding of Machine Learning: In-depth knowledge of machine learning techniques (SVM, decision trees, clustering, and more), particularly as applied to computer vision and autonomous driving.
• Mathematical & Statistical Expertise: Extensive knowledge of linear algebra, optimization techniques, probability, and statistics.
• Communication & Leadership: Excellent written and oral communication skills, with the ability to collaborate with diverse teams and lead projects.

Preferred Qualifications:

• Extensive experience with 3D point clouds from LiDAR, RADAR, or similar sensors.
• Expertise in robotic algorithms, including Kalman filters and other tracking methods.
• Deep learning experience for computer vision applications (e.g., object detection, segmentation).
• Experience in autonomous driving perception systems and understanding of the challenges of integrating multi-sensor data (LiDAR, cameras, RADAR).