Pony AI Inc. (“Pony.ai”) is a global leader in the large-scale commercialization of autonomous mobility. Leveraging its vehicle-agnostic Virtual Driver technology, full-stack autonomous driving technology that seamlessly integrates its proprietary software, hardware, and services, Pony.ai is developing a commercially viable and sustainable business model that enables the mass production and deployment of vehicles across transportation use cases. Founded in 2016, Pony.ai has expanded its presence across China, Europe, East Asia, the Middle East, and other regions, ensuring widespread accessibility to its advanced technology. Pony.ai is among the first in China to obtain licenses to operate fully driverless vehicles in all four Tier-1 cities in China (Beijing, Guangzhou, Shanghai, Shenzhen) and has begun to offer public-facing, fare-charging robotaxi services without safety drivers in Beijing, Guangzhou and Shenzhen. Pony.ai operates a fleet consisting of over 250 robotaxis. To date, Pony.ai has driven nearly 45 million autonomous testing and operation kilometers on open roads worldwide.
Open Positions
Software Engineer - Perception Infrastructure
BS/MS or Ph.D in Computer Science or a related field, Strong programming skills in C++, and Experience in the autonomous driving domain is preferred
Research Intern (Deep Learning), 2026 Spring (Master/PhD)
Master's or PhD program in Computer Science or related field, strong background in deep learning, and proficiency in software design and development
Software Engineer, Deep Learning
Master's degree in Computer Science or equivalent industry experience, and solid understanding of data structures and algorithms
MLE Intern, ML Runtime & Optimization (Spring 2026, Master/PhD)
Master's or PhD program, strong programming skills, and experience in benchmarking and profiling
Machine Learning Engineer, ML Runtime & Optimization
Bachelor's degree in computer science or related field, strong programming skills in C/C++ or Python, experience in model optimization and efficient deep learning techniques