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Xpengmotors in Santa Clara is seeking a Senior Staff Machine Learning Engineer to lead the design of end-to-end VLA architectures and develop generative world models for autonomous vehicles.
The position requires 5-8 years of expertise in deep learning with a focus on innovative solutions for intelligent mobility. You’ll work in a supportive environment on cutting-edge technologies aimed at pioneering advancements in autonomous driving.
Competitive compensation, snacks, and benefits are offered.
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 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.
The base salary range for this full‑time position is $244,140-$413,160, in addition to bonus, equity and benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
We are an Equal Opportunity Employer. It is our policy to 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 prescribed category set forth in federal or state regulations.