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: Vision-Language-Action (VLA) models and foundation models are becoming increasingly important in autonomous driving, but turning research models into real-time, production-ready systems on vehicle hardware remains a major challenge. We are looking for an entry-level engineer or intern to support the optimization and deployment of multimodal models onto vehicle-grade compute platforms. This role is a strong fit for candidates who are excited about deep learning systems, model deployment, and edge inference for real-world autonomous driving applications.
Key Responsibilities
- Support model quantization and deployment efforts for large-scale multimodal models, including Transformers and vision-language models.
- Assist with applying model optimization techniques such as post-training quantization, quantization-aware training, pruning, and related compression methods under guidance from senior engineers.
- Work with research and platform teams to help improve model deployability and understand hardware and runtime constraints.
- Contribute to deployment tools, test pipelines, and runtime modules in C++ and Python for autonomous driving systems.
- Help analyze model performance, memory usage, latency, and numerical accuracy across different deployment targets.
- Participate in debugging and performance tuning across the model, runtime, and system stack.
- Support validation and testing workflows to ensure stable and reliable deployment in vehicle and simulation environments.
Basic Qualifications
- BS, MS, or PhD in Computer Science, Electrical Engineering, Robotics, or a related field.
- Strong programming skills in C++ and/or Python.
- Familiarity with deep learning frameworks such as PyTorch.
- Basic understanding of model inference, deployment, or optimization workflows using tools such as ONNX, TensorRT, or similar frameworks.
- Exposure to model compression or quantization concepts such as INT8, FP16, or related approaches.
- Interest in computer architecture, performance optimization, and edge or embedded systems.
- Strong problem-solving skills and the ability to learn quickly in a fast-paced engineering environment.
- Good communication skills and the ability to collaborate with cross-functional teams.
Preferred Qualifications
- Internship, research, or project experience in deep learning model deployment, inference acceleration, or embedded AI.
- Familiarity with Transformers, multimodal models, or foundation models.
- Experience with CUDA or GPU programming.
- Exposure to autonomous driving, robotics, or real-time systems.
- Contributions to research projects, open-source repositories, or relevant course projects.
What do we provide:
- A fun, supportive and engaging environment.
- Infrastructures and computational resources to support your work.
- Opportunity to work on cutting edge technologies with the top talents in the field.
- Opportunity to make significant impact on the transportation revolution by the means of advancing autonomous driving.
- Competitive compensation package.
- Snacks, lunches, dinners, and fun activities.
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.