MLE Intern, ML Runtime & Optimization (Spring 2026, Master / PhD)

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 Nov. 2024.

Responsibility

The ML Infrastructure team at Pony.ai provides a set of tools to support and automate the lifecycle of the AI workflow, including model development, evaluation, optimization, deployment and monitoring.

As a Machine Learning Engineer Intern in ML Runtime & Optimization, you will be developing technologies to advance the training and inferences of the AI models in autonomous driving systems.

This includes :

• Performing in-depth analysis and optimization to model training and deployment to achieve the state of art in performance and efficiency in autonomous driving.
• Work across the entire AI framework / compiler stack (e.g. Torch, CUDA and TensorRT), support model development and prototype key deep learning algorithms.
• Analyze the tradeoffs between performance, cost and energy for autonomous driving.
• Collaborating closely with diverse groups in Pony.ai to influence the next-generation compute platform HW and SW design.
• Research the latest model architectures, programming models and hardware.
• Currently pursuing a Masters or PhD program or a related discipline.
• Strong programming skills in C / C++ or Python.
• Solid understanding of CPU or GPU execution model, e.g. threads, registers, cache, memory, cost and performance trade-off, etc.
• Experience in benchmarking, profiling and validating performance.
• Strong communication skills and ability to work cross-functionally between software and hardware teams

Preferred Qualifications :

One or more of the following fields are preferred

Note

Compensation