Are you looking to be part of a fun, creative company at the forefront of groundbreaking technologies? NVIDIA is leading the way in some of the world's most exciting tech areas, including Virtual Reality, Artificial Intelligence, Deep Learning, and Autonomous Vehicles. We are widely recognized as one of the most desirable employers, with some of the most forward-thinking individuals in the industry working with us. There has never been a more exciting time to join our team!

We are seeking a highly motivated Decision Making and Planning Software Intern to join our team. In this role, you will have the opportunity to delve into cutting-edge autonomous driving full stack technology, focusing on decision-making and planning algorithms within the NDAS (NVIDIA DRIVE AV Solution) product. You will address and debug the limitations and boundaries of the current NDAS decision-making and planning components both for driving and parking functions (APA, MPA, L2+, L2++), particularly in relation to Chinese traffic laws and challenging scenarios. You will collaborate with various teams to propose robust software solutions, making these components more competitive in the Chinese market.

• Collaborate with engineers from different NDAS teams to complete simulation/in-car testing and triage NDAS driving and parking decision-making and planning issues.
• Participate in performance tuning of NDAS decision-making and planning algorithms in complex traffic scenarios for driving and parking functions.
• Integrate, adapt, and tune more competitive NDAS decision-making and planning solutions on different OEM partners' platforms.
• Track cutting-edge academic research results related to decision-making and planning domains and quickly apply the latest research results in C++ coding implementation.

• Availability to work 4 days a week for at least 4 months.
• Master's or Ph.D. degree in Computer Science, Robotics, Automotive Engineering, Electrical Engineering, Mechanical Engineering, or a related field.
• Strong knowledge of programming and debugging techniques, proficiency in C++ and Python, and experience in data structures and advanced algorithms.
• Familiarity with basic planning algorithms and control system designs, such as LQR, classical feedback controllers, optimal control, occupancy grids, Dijkstra search, A*, and Random Root Trees (RRTs).
• Strong teamwork spirit and excellent communication skills.

• Publications in top conferences/journals in a related field or equivalent experiences.
• Rich experience in autonomous vehicles and autonomous robot competitions, with awards being a bonus.
• Extensive experience in at least one of the following: robotics research in motion planning, trajectory optimization, planning under uncertainty, probabilistic robotics, data analysis at scale, or machine learning at scale.