Senior Staff Machine Learning Engineer – End-to-End Autonomous Driving

Confirmed live in the last 24 hours

XPENG Motors

Compensation

$244,140 - $413,160/year

Santa Clara, CA

On-site

Posted March 18, 2026

Job Description

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. With a strong focus on intelligent mobility, XPENG is dedicated to reshaping the future of transportation through cutting-edge R&D in AI, machine learning, and smart connectivity.

We are seeking Deep Learning Engineers with strong expertise in machine learning (ML) and deep learning (DL) system design, along with solid software development skills. In this role, you will research, implement, and evaluate a unified end-to-end onboard model leveraging state-of-the-art technologies, including transformer-based architectures, diffusion models, reinforcement learning, and Vision-Language-Action (VLA) models. You will collaborate with a world-class team of experts in computer vision, AI systems, and software engineering to push the boundaries of autonomous vehicle performance. Your work will be powered by vast amounts of real-world multimodal data from our autonomous fleet, enabling the development of next-generation AI-driven driving solutions.

Job 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.
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