Machine Learning Engineer – End-to-End Autonomous Driving

Overview

XPENG is a leading smart technology company at the forefront of innovation, integrating advanced AI and autonomous driving technologies into its vehicles, including electric vehicles (EVs), electric vertical take-off and landing (eVTOL) aircraft, and robotics. XPENG focuses on intelligent mobility and reshapes the future of transportation through AI, machine learning, and smart connectivity.

We are seeking Deep Learning Engineers with strong expertise in ML/DL system design and solid software development skills to research, implement, and evaluate a unified end-to-end onboard model using state-of-the-art technologies (transformer-based architectures, diffusion models, reinforcement learning, and Vision-Language-Action models). You will collaborate with a world-class team to push autonomous vehicle performance using real-world multimodal data from XPENG’s autonomous fleet.

• Research and develop cutting-edge deep learning algorithms for a unified, end-to-end onboard model that integrates perception, prediction, and planning, replacing traditional modular model pipelines.
• Research and develop Vision-Language-Action (VLA) models for context-aware, multimodal decision-making.
• Design and optimize efficient neural network architectures for low-latency, real-time execution on the vehicle’s high-performance computing platform.
• Develop and scale offline ML infrastructure to support rapid adaptation, large-scale training, and continuous self-improvement using self-supervised learning, imitation learning, and reinforcement learning.
• Deliver production-quality onboard software, collaborating with sensor fusion, mapping, and perception teams.
• Leverage large real-world datasets from the autonomous fleet to train and refine end-to-end driving models with multimodal data.
• Design, conduct, and analyze large-scale experiments, including sim-to-real transfer, closed-loop evaluation, and real-world testing.
• Collaborate with system software engineers to deploy high-performance DL models on embedded automotive hardware for robustness under diverse driving conditions.
• Work cross-functionally with AI researchers, computer vision experts, and autonomous driving engineers to advance end-to-end learning and transformer/diffusion/ RL approaches for autonomous mobility.

• MS or PhD in Engineering or Computer Science focusing on Deep Learning or AI, or equivalent experience.
• Strong experience in applied DL including model architecture design, training, data mining, and data analytics.
• 3–5+ years of experience with DL frameworks such as PyTorch or Tensor Flow.
• 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.

• Hands-on experience developing a DL-based planning engine for autonomous driving.
• Experience applying CNN/RNN/GNN, attention models, or time-series analysis to real-world problems.
• Experience in other ML/DL applications, e.g., reinforcement learning.
• Experience with DL model deployment and optimization tools such as ONNX and Tensor
  
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The base salary range for this full-time position is $215,280-$364,320, plus bonus, equity and benefits. Salaries are determined by role, level, and location and reflect minimum/maximum targets for new hires across US locations.

We are an Equal Opportunity Employer. We provide equal employment opportunities to all qualified persons without regard to race, age, color, sex, sexual orientation, religion, national origin, disability, veteran status or marital status or any other category protected by law.