Senior Staff Machine Learning Engineer – Autonomous Driving Foundation Models
About The Position
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.
Requirements
- 5-7 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.
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.
Benefits
- 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
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
