Machine Learning Engineer Intern
Posted on 3/29/2024
XPeng Motors

1,001-5,000 employees

Designs and manufactures intelligent electric vehicles and aircrafts
Company Overview
XPENG stands out as a leader in the tech industry, with its focus on intelligent mobility solutions such as electric vehicles and eVTOL aircraft, demonstrating a competitive edge in the rapidly evolving transportation sector. The company's proprietary Advanced Driver Assistance System (XPILOT) and intelligent operating system (Xmart OS) enhance the user experience by integrating technology and mobility, positioning XPENG as a pioneer in smart, people-first mobility. The company's culture fosters technological advancement, making it an exciting workplace for those passionate about shaping the future of transportation.
Data & Analytics
Robotics & Automation
Consumer Software
AI & Machine Learning

Company Stage


Total Funding





Guang Zhou Shi, China

San Diego, CA, USA
Experience Level
Desired Skills
Data Structures & Algorithms
AI & Machine Learning
Applied Machine Learning
Robotics & Autonomous Systems
Deep Learning
  • PhD or Master in computer science, electrical engineering, or other related fields.
  • Experience in working with CNNs/RNNs and generative models such as LLMs, diffusion models.
  • Programming fluently in Python with a deep understanding of software design, programming techniques, and algorithms.
  • Master one of several mainstream machine learning development framework such as TensorFlow, PyTorch, MXNet.
  • Research areas encompass a wide range of topics in the areas of Foundation Models, such as building LLM from scratch, RLHF, multi-model generative modeling, scalable end-to-end models for Autonomous Driving etc.
  • Work with cross teams to deploy machine learning solutions to continuously improve SOTA performance for multiple perception tasks, e.g., object detection, segmentation, etc.
  • Deliver pre-trained and finetuned models to achieve the most intelligent autonomous driving system in class.
  • Work with massive image and video label/unlabeled dataset to build a robust and scalable data driven model.