Staff Software Engineer

XPENG•Santa Clara, CA

14d

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.

Requirements

BA/BS degree in Computer Science, related field or equivalent practical experience.
Delivered a company scale and industry leading infrastructure from scratch.
Expert level understanding of K8S, Queueing and in memory data structures.
10+ years developing backend services (we primarily write C++ and Python).
3+ experience working on complex Machine learning infrastructure.
Experience developing and maintaining machine learning production systems deployed to the cloud and on premise.
Experience in using Bazel for Complex large scale machine learning infrastructure.
Experience with modern python tooling like Ruff, Mypy, Typeguard and pytest.
Experience with Supporting MLOps.
Experience working in a fast paced environment.
Self motivated and ability to deal with ambiguity and evolving requirements.

Nice To Haves

Experience of working on Autonomous vehicle stack.
Experience in the automotive industry.
Experience in utilizing MCP and related tooling to improve infrastructure usage.
Strong experience in designing and implementing highly horizontally-scalable architecture.
Developing, deploying and monitoring cloud infrastructure is a strong plus.

Responsibilities

Design , Architect and implement company scale distributed system for next generation of the autonomy software evaluation.
Collaborate with multiple teams in side XPENG to deliver best in class infrastructure for next generation of XPENG innnovations.
Demonstrate a can-do attitude and able to thrive at a high pace, always evolving landscape of requirements.
Collaborate with stake holders to deliver highly complex and flexible infrastructure to meet their use cases, SLA and QOS.
Design and implement tools and infrastructure to improve engineering efficiency of machine learning engineers daily workflows.
Design and implement complex workflow on cloud and on premise infrastructure to provide insights into Software Quality and Release Readiness of features.
Leverage LLMs to bring efficiency to existing established processes of triaging, analysis and troubleshooting.