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Senior Staff Machine Learning Engineer – Autonomous Driving Foundation Models

XPENG
CAN
Senior · 5–8 years experience
USD 244140-413160 / year
Discovered 1 months ago
pytorch

Full Job Posting

  • 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

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