Full Time Software Engineer Intern (Mapping-Calibration) Jobs, salary up to SGD 6,000 in MOTIONAL SINGAPORE PTE. LIMITED - Ricebowl

We are seeking highly motivated Software Engineer Intern to join our team to conduct cutting-edge research and development in Localization, Mapping and Calibration. This is an exceptional opportunity to contribute directly to the advancement of autonomous vehicle technology, working alongside a team that is leading the industry in location, mapping and calibration innovation.

What you’ll be doing:

• Conduct advanced research and development on state-of-the-art state estimation problems, specifically focusing on scalability across city and multi-city environments. Research topics including, but not limited to: SLAM methods for city-scale mapping technologies, outdoor sensor calibration technologies, etc.
• Work with large-scale internal datasets to demonstrate the effectiveness of your research.
• Collaborate with different teams to deploy your research results onto autonomous vehicles.
• Publish the research results in papers and patents.

What we are looking for:

• Bachelor's, Master's or PhD program in Computer Science, Electrical Engineering, Robotics, or a similar technical field of study.
• Strong research background in the field of SLAM, state estimation, and/or sensor calibration, with publications in any of the top-tier conferences (e.g., CVPR, ICCV, ECCV, ICRA, IROS, RSS).
• Experience in at least one of the following: applying machine learning techniques to mapping or geospatial problems, working with large-scale mapping datasets or systems, utilizing online calibration techniques for autonomous driving or robotics systems, or demonstrating a strong theoretical and practical background in computer vision (especially sensor calibration/SLAM).
• Strong communication and interpersonal skills.
• Proficiency in C++/Python coding, having experience in onboard programming, with an understanding of real-time systems is a plus.
• Previous experience in autonomous driving is preferred.