Staff Machine Learning Engineer – Autonomous Driving Model Quantization & Deployment

Confirmed live in the last 24 hours

XPENG Motors

Compensation

$215,280 - $364,320/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: The challenge of Vision-Language-Action (VLA) models and Foundation Models isn't just their intelligence—it's their real-time execution at the edge. We are seeking a high-caliber Staff Machine Learning Engineer to bridge the gap between massive research models and production-ready L4 autonomous driving systems. You will lead the effort to optimize and deploy our VLA models onto vehicle-grade compute platforms for our global fleet.

Key Responsibilities:

Lead Optimization Strategy: Own the end-to-end quantization and optimization roadmap for large-scale multimodal models (Transformers, VLMs).
Model Compression: Apply and innovate in PTQ (Post-Training Quantization), QAT (Quantization-Aware Training), and pruning techniques to fit VLA models into strict memory and power envelopes.
Hardware-Software Co-design: Collaborate directly with model researchers to ensure architectures are "deployment-friendly" and with platform teams to influence future hardware requirements.
Production Excellence: Develop and maintain robust, safety-critical deployment stacks in Modern C++, ensuring 24/7 stability and deterministic performance on the road.