Machine Learning Research Intern
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Job Description
About Company:
Founded in 2016 in Silicon Valley, Pony.ai is a global leader in autonomous mobility. They are pioneers in extending autonomous mobility technologies and services worldwide with Robotaxi, Robotruck and Personally Owned Vehicles (POV) business units. Pony.ai went public at NASDAQ in Nov. 2024.
Job Description:
This position involves working with experts in self-driving vehicles to design and develop large-scale foundation models trained on vast amounts of real-world data. The role requires framing open-ended real-world problems into well-defined ML problems, developing and applying cutting-edge ML approaches (deep learning, reinforcement learning, imitation learning, etc), scaling them to data pipelines, and streamlining them to run in real-time on the cars.
Responsibilities:
• Develop and deploy deep learning models, including vision language models (VLMs) and Large Language Models (LLMs).
• Design and implement multi-modality and multi-task perception models focusing on 3D object detection and tracking, segmentation, semantics understanding, video understanding, scene understanding, traffic control, or trajectory prediction, etc.
• Optimize deep learning models to run robustly under tight run-time constraints.
Requirements:
• Currently pursuing a Masters or PhD program in Computer Science, Machine Learning, Robotics, or similar field.
• Strong background in deep learning, with experience in model design, training and evaluation.
• Experience with deep learning research and tools.
• Proficiency in software design and development using Python and C++.
• Experience working with large-scale datasets, data preprocessing, and pipeline management.
Preferred Experience:
• Publications on top-tier conferences like CVPR/ICCV/ECCV/ICLR/ICML/NeurIPS/ICLR/AAAI
• Experience in applying ML/DL for behavior prediction, imitation learning, motion planning.
• Experience in deploying deep learning algorithms for real time applications, with limited computing resources.
• Experience in convex optimization, computational geometry or linear algebra.
• Experience in GPU/CUDA/TensorRT
• Previous internships involving large-scale deep learning models and systems
• Preferred graduate before Aug 2026
Note: This is a fully onsite position in Fremont, CA, for at least 3 months.
Compensation:
• Master: $7000/month
• PhD: $10,000/month
