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: 5-8 years of expertise in Deep Learning, with a significant track record in VLM, VLA , or Embodied AI . Proven experience in training and deploying Foundation Models (Transformers, LLMs) at scale. Deep understanding of Sequential Decision Making , World Models, or Policy Gradient methods. Mastery of PyTorch and expertise in distributed training (DeepSpeed, Megatron, etc.). A "Product-First" mindset: The ability to balance cutting-edge research with the deterministic requirements of L4 production vehicles. What do we provide: A fun, supportive and engaging environment Infrastructures and computational resources to support your ML model development/research. Opportunity to work on cutting edge technologies with the top talent in the field. Opportunity to make significant impact on transportation revolution by the means of advancing autonomous driving Competitive compensation package Snacks, lunches, dinners, and fun activities 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.

Requirements

Must-haves

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.
5-8 years of expertise in Deep Learning, with a significant track record in VLM, VLA , or Embodied AI .
Proven experience in training and deploying Foundation Models (Transformers, LLMs) at scale.
Deep understanding of Sequential Decision Making , World Models, or Policy Gradient methods.
Mastery of PyTorch and expertise in distributed training (DeepSpeed, Megatron, etc.).
A "Product-First" mindset: The ability to balance cutting-edge research with the deterministic requirements of L4 production vehicles.
A fun, supportive and engaging environment
Infrastructures and computational resources to support your ML model development/research.
Opportunity to work on cutting edge technologies with the top talent in the field.
Opportunity to make significant impact on transportation revolution by the means of advancing autonomous driving
Competitive compensation package
Snacks, lunches, dinners, and fun activities

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningml

Platform: greenhouse
Effort: quick

Benefits

Benefits detected in the listing.

Vision