Staff Software Engineer - AI Infrastructure

XPENG•Santa Clara, CA

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. We are looking for a versatile Machine Learning Infrastructure Engineer to join XPeng’s Fuyao AI Platform team — a core AI infrastructure powering autonomous driving, robotics, and intelligent cockpit applications. You will build and optimize next-generation AI infrastructure, spanning dataloader, dataset and data production systems, large-scale inference, and distributed compute platforms — with a strong focus on efficiency, scalability, and reliability.

Requirements

Master’s degree in Computer Science, Software Engineering, or equivalent experience.
5+ years of experience in large-scale data processing or ML infrastructure.
Proficient in Python with solid software engineering fundamentals, clean coding practices, and strong debugging skills.
Hands-on experience with relational databases and NoSQL systems, including metadata and cache management; prior experience with large-scale VectorDB is highly desirable.
Familiarity with Linux file systems and network I/O optimization for distributed or object storage.
Strong communication skills and ability to work cross-functionally in fast-paced environments.
Strong ability to learn quickly, adapt to new challenges, and proactively explore and adopt new technologies.

Nice To Haves

Familiarity with the autonomous driving industry and enthusiasm for its challenges.
Experience with distributed computing frameworks such as Ray, Flink or Spark.
Experience in building and scaling ML infrastructure in cloud-native environments.
Large-scale deep learning training or inference optimization focused on scalability and model acceleration.
Columnar storage formats (Parquet/ORC) and related ecosystems, including partitioning, compression, and vectorized I/O optimization.
Large-scale data loading frameworks (PyTorch Dataloader, Hugging Face Datasets).

Responsibilities

Design and optimize large-scale data processing, production and loading pipelines, supporting heterogeneous data types (images, videos, point clouds, sensor streams, etc.).
Build and maintain high-performance dataset management and loading frameworks, ensuring low-latency, high-throughput pipelines for training and inference.
Develop and optimize distributed compute and inference systems, including scheduling, resource utilization, and performance tuning.
Collaborate with cross-functional teams (e.g. Algorithms, Data Lakehouse) to translate requirements into production-ready infrastructure solutions.
Continuously monitor, profile, and eliminate bottlenecks across AI data, inference and compute stack.