(Senior) Software Engineer - Behavior

pony.ai•Fremont, CA

10d

About The Position

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.

Requirements

BS in Computer Science, ML, Robotics, similar technical field of study, or equivalent practical experience
2+ years hands-on general software engineering experience, proficient in at least one mainstream programming language (such as C, C++, Python, or Java) and familiar with common data structures and algorithms.
Ability to lead ambiguous technical work from problem framing through reliable delivery.

Nice To Haves

Background in autonomous vehicles, robotics, or complex simulation environments.
General software engineering experience solving motion planning or related robotics problems

Responsibilities

Designing and developing high-performance, highly reliable Planning architectures and systems.
Developing and optimizing high-precision, high-performance computational geometry and numerical computing algorithms.
Enhancing autonomous vehicle decision-making and driving capabilities using machine learning and advanced algorithms.
Designing and building scalable data processing infrastructure and systems for large-scale road-test data.
Analyze large amounts of data of the real world and simulate driving, understanding, characterizing and clustering the performance of virtual drivers.
Developing internal tools to optimize engineering workflows and improve R&D efficiency.
Work cross-functionally on safety, road rules compliance, and ride quality