Software Engineer, Decision Making & Path Planning

Founded in 2016 in Silicon Valley, Pony.ai has quickly become a global leader in autonomous mobility and is a pioneer in extending autonomous mobility technologies and services at a rapidly expanding footprint of sites around the world. Operating Robotaxi, Robotruck and Personally Owned Vehicles (POV) business units, Pony.ai is an industry leader in the commercialization of autonomous driving and is committed to developing the safest autonomous driving capabilities on a global scale. Pony.ai's leading position has been recognized, with CNBC ranking Pony.ai #10 on its CNBC Disruptor list of the 50 most innovative and disruptive tech companies of 2022. In June 2023, Pony.ai was recognized on the XPRIZE and Bessemer Venture Partners inaugural "XB100" 2023 list of the world's top 100 private deep tech companies, ranking #12 globally. As of August 2023, Pony.ai has accumulated nearly 21 million miles of autonomous driving globally. Pony.ai went public at NASDAQ in November 2024.

Responsibility

The team is responsible for determining how autonomous vehicles navigate based on real-time traffic conditions, including roads, surrounding environments, other vehicles, and pedestrians. It is one of the most core teams in autonomous driving. You will be responsible for one or more of the following tasks:

• Architecture design, algorithm, and system development for high-performance and highly reliable L4 Autonomous Driving Planning algorithms.
• Optimizing and providing safety fallbacks for end-to-end model outputs. Through post-processing, ensure vehicle behavior is safe and controllable, realizing an HD-map-independent autonomous driving system that satisfies L4 capabilities and requirements.
• Designing and developing a redundant system that satisfies L4 capabilities and requirements using lower compute power, select sensors, and end-to-end technology.
• Architecture design and system development for road-test big data analysis and processing.
• Simulation platform development, using massive road-test data to make the simulation platform better align with real-world road test scenarios.