Staff Machine Learning Engineer – AI Foundation

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 looking for a full‑time Staff Machine Learning Engineer – AI Foundation, with deep knowledge and strong enthusiasm towards establishing a state‑of‑art ML infrastructure for training very large foundation model and accelerating model training / inference. Our mission is to solve the autonomous driving problem. You will work with a team of talented software engineers, machine learning engineers and research scientists to push the boundary of state‑of‑art machine learning models which will enable the next‑generation E2E solution of autonomous driving.

Job Responsibilities

• Design, train, and deploy large deep learning models that can leverage the vast amount of labeled and unlabeled data from a fleet of million vehicles.

Minimum Skill Requirements

Preferred Skill Requirements

What Do We Provide

The base salary range for this full‑time position is $215,280-$364,320, 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.

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