Research Engineer Intern - Mapping & Localization

PlusAI is a Physical AI company pioneering AI-based virtual driver software for factory-built autonomous trucks. Headquartered in Silicon Valley with operations in the United States and Europe, Plus was named by Fast Company as one of the World’s Most Innovative Companies. Partners including TRATON GROUP’s Scania, MAN, and International brands, Hyundai Motor Company, Iveco Group, Bosch, and DSV are working with Plus to accelerate the deployment of next-generation autonomous trucks. If you’re ready to make a huge impact and drive the future of autonomy, Plus is looking for talented individuals to join its fast-growing teams. Responsibilities:

LiDAR Algorithm Development: Design, develop, and optimize cutting-edge LiDAR-based localization and mapping algorithms to enhance the autonomy of vehicles.

Map Support: Refine both online and offline map generation algorithms to improve and sustain localization precision.

Map Interface Enhancement: Advance the functionality and user experience of the LiDAR map interface, focusing on the robust management of various map versions.

Sensor Fusion Development: Skillfully merge LiDAR data with other sensory inputs (e.g., GPS, IMU, cameras) to devise integrated, real-time mapping and localization solutions.

Continue to grow within the team, taking on more complex projects and pushing the boundaries of autonomous vehicle technology.

Required Skills:

Education: MS or PhD in Computer Science, Electrical Engineering, Robotics, or a related discipline.

Technical Experience: Proven project involvement in LiDAR-based localization and mapping within autonomous vehicles or robotics.

Sensor Fusion Expertise: Deep understanding of integrating various sensors like LiDAR, GPS, IMU, and camera data.

Software Proficiency: Solid experience in C++ and ROS.

Communication: Exceptional ability to communicate complex technical details effectively across multidisciplinary teams, concepts to non-technical team members.

Preferred Skills:

Experience with Gaussian splatting/NeRF is a plus.

Experience with deep learning techniques applied to localization and mapping is a plus.