Senior Machine Learning Engineer - AI Foundation
About this role
The AI Foundation team at XPENG builds advanced machine learning infrastructure to train very large foundation models and accelerate model training and inference. Engineers in this group focus on solving the autonomous driving problem by enabling next-generation end-to-end solutions for electric vehicles, aircraft, and robotics. Daily work centers on designing data pipelines, implementing training frameworks for physical AI models, and optimizing performance across cloud and edge systems.
Successful candidates bring deep technical expertise in machine learning systems and a background in scaling distributed model training. The group operates within a collaborative environment supported by significant computational resources and top-tier engineering talent. Joining the organization allows practitioners to directly influence the future of intelligent mobility and transportation technology.
What you will do
- Design and implement scalable training data pipelines that stream hundreds of petabytes of data from a large fleet of vehicles.
- Build out robust training frameworks for physical AI foundation models including VLA 2.0, XWorld, and robotics applications.
- Accelerate training processes by utilizing advanced parallelism techniques such as FSDP, Expert Parallel, and Context Parallel.
- Optimize cloud-based model inference workflows to support closed-loop simulation, reinforcement learning, and enterprise vision-language models.
What you need
- Hold a Master's degree in Computer Science, Computer Engineering, Electrical Engineering, or possess equivalent industry experience.
- Demonstrate deep knowledge and practical working experience with the PyTorch framework.
- Understand model inference frameworks such as vLLM and SGLang.
- Possess in-depth knowledge of transformer architecture alongside methods to accelerate transformer training and inference.
- Bring proven experience executing large-scale distributed model training in production environments.
- Show a documented track record of profiling machine learning models and performing optimization to improve training and inference speed.
Nice to have
- Previous experience in the autonomous driving industry
- Experience with CUDA language for writing custom ops
- Experience with edge computing systems
- Knowledge of distributed computing frameworks, such as Ray
- A track record of efficiently solving complex problems collaboratively on larger teams
Benefits
- Competitive compensation package
- Bonus, equity, and benefits
- Snacks, lunches, dinners, and fun activities
- Infrastructures and computational resources to support your work
- A fun, supportive and engaging environment
Skills and tech stack
Questions candidates ask
What is the salary range for this role?
The base salary range for this full-time position is $174,720 to $295,680. Additional compensation includes bonus, equity, and benefits.
How is the individual salary determined within the range?
Individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
What technologies and frameworks will I be working with?
You will work with PyTorch, transformer architectures, model inference frameworks like vLLM and SGLang, and various distributed training parallelisms.
What kind of projects does the team work on?
The team works on establishing machine learning infrastructure for training very large foundation models and enabling the next-generation end-to-end solution of autonomous driving.