Senior Staff Machine Learning Engineer – Autonomous Driving Foundation Models

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.

The Mission: We are building the next generation of L4 autonomous vehicles. Moving beyond traditional modular stacks, we are developing large-scale Vision-Language-Action (VLA) models and World Models to handle the infinite long-tail scenarios of global driving. As a Senior Staff Machine Learning Engineer, you will architect the transition from behavior cloning to intelligent, zero-shot decision-making in diverse global markets.

Key Responsibilities:

Architectural Leadership: Lead the design of end-to-end VLA architectures, bridging multi-modal perception with high-level linguistic reasoning and precise action generation.
World Model Development: Drive R&D in generative world models (latent dynamics) to create high-fidelity, controllable driving simulations for closed-loop training and evaluation.
Policy Evolution: Apply Advanced RL (Online/Offline) and IL to refine driving policies, focusing on long-horizon planning and complex multi-agent interactions.
Scaling & Data Strategy: Define scaling laws for driving foundation models, overseeing data curation, automated labeling, and post-training at a multi-billion parameter scale.
Global Generalization: Lead the model’s adaptation strategy for overseas road conditions, ensuring robust performance across varying traffic laws and driving cultures.

Qualifications:

5-7 years of expertise in Deep Learning, with a significant track record in VLM, VLA, or Embodied AI.
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