Full-Time

Staff Machine Learning Engineer

End-to-End Autonomous Driving

Confirmed live in the last 24 hours

XPENG Motors

XPENG Motors

1,001-5,000 employees

Designs and manufactures intelligent electric vehicles and aircrafts

Compensation Overview

$215.3k - $364.3k/yr

+ Bonus + Equity

Senior

Santa Clara, CA, USA + 1 more

More locations: San Diego, CA, USA

Category
Deep Learning
Computer Vision
AI & Machine Learning
Required Skills
Python
Tensorflow
Data Structures & Algorithms
Pytorch
Machine Learning
Computer Vision
Data Analysis
Reinforcement Learning
Requirements
  • MS or PhD level education in Engineering or Computer Science with a focus on Deep Learning, Artificial Intelligence, or a related field, or equivalent experience.
  • Strong experience in applied deep learning including model architecture design, model training, data mining, and data analytics.
  • 3 - 5 years + of experience working with DL frameworks such as PyTorch, Tensorflow.
  • Strong Python programming experience with software design skills.
  • Solid understanding of data structures, algorithms, code optimization and large-scale data processing.
  • Excellent problem-solving skills.
Responsibilities
  • Research and develop cutting-edge deep learning algorithms for a unified, end-to-end onboard model that seamlessly integrates perception, prediction, and planning, replacing traditional modular model pipelines.
  • Research and develop Vision-Language-Action (VLA) models to enable context-aware, multimodal decision-making, allowing the model to understand visual, textual, and action-based cues for enhanced driving intelligence.
  • Design and optimize highly efficient neural network architectures, ensuring they achieve low-latency, real-time execution on the vehicle’s high-performance computing platform, balancing accuracy, efficiency, and robustness.
  • Develop and scale an offline machine learning (ML) infrastructure to support rapid adaptation, large-scale training, and continuous self-improvement of end-to-end models, leveraging self-supervised learning, imitation learning, and reinforcement learning.
  • Deliver production-quality onboard software, working closely with sensor fusion, mapping, and perception teams to build the industry’s most intelligent and adaptive autonomous driving system.
  • Leverage massive real-world datasets collected from our autonomous fleet, integrating multi-modal sensor data to train and refine state-of-the-art end-to-end driving models.
  • Design, conduct, and analyze large-scale experiments, including sim-to-real transfer, closed-loop evaluation, and real-world testing to rigorously benchmark model performance and generalization.
  • Collaborate with system software engineers to deploy high-performance deep learning models on embedded automotive hardware, ensuring real-world robustness and reliability under diverse driving conditions.
  • Work cross-functionally with AI researchers, computer vision experts, and autonomous driving engineers to push the frontier of end-to-end learning, leveraging advances in transformer-based architectures, diffusion models, and reinforcement learning to redefine the future of autonomous mobility.
Desired Qualifications
  • Hands on experience in developing DL based planning engine for autonomous driving.
  • Experience in applying CNN/RNN/GNN, attention model, or time series analysis to real world problems.
  • Experience in other ML/DL applications, e.g., reinforcement learning.
  • Experience in DL model deployment and optimization tools such as ONNX and TensorRT.

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.

Company Size

1,001-5,000

Company Stage

N/A

Total Funding

$8.2B

Headquarters

Guang Zhou Shi, China

Founded

2014