Senior Machine Learning Engineer

Responsibilities

• Design and implement training data pipelines streaming labeled and unlabeled data at hundreds-of-petabytes scale from more than one million vehicles.
• Implement training frameworks for XPENG physical AI foundation models, including VLA 2.0, XWorld, and Robotics.
• Accelerate model training using FSDP, Expert Parallel, Context Parallel, and advanced data types.
• Accelerate cloud model inference for closed-loop simulation, reinforcement learning, and enterprise LLM/VLM applications.
• Profile models and investigate performance bottlenecks to improve training and inference speed.

Requirements

• Master's degree in computer science, computer engineering, or electrical engineering, or equivalent industry experience.
• Deep knowledge of PyTorch.
• Knowledge of model inference frameworks such as vLLM or SGLang.
• In-depth knowledge of transformer architecture and techniques for accelerating transformer training and inference.
• Experience with large-scale distributed model training.
• Preferred experience in the autonomous driving industry.
• Preferred experience writing custom operations with CUDA.
• Preferred experience with edge computing systems.
• Preferred knowledge of distributed computing frameworks such as Ray.
• Ability to solve complex problems collaboratively on larger teams.

Benefits

• Full-time position with a supportive and engaging work environment.
• Access to infrastructure and computational resources to support the work.
• Opportunity to work with leading talent on cutting-edge technology and impact autonomous driving.
• Competitive compensation package with bonus, equity, and benefits.
• Snacks, lunches, dinners, and fun activities.