AI Infra Onboard Performance Intern

XPENG; Santa Clara (CA); On-site; USD 100,000 - 150,000; Full time

Benefits offered by this job

Supportive environment

Computational resources

Opportunities for growth

Competitive compensation

Snacks, lunches, dinners

Job summary

XPENG, located in Santa Clara, California, is seeking a talented individual to design and optimize software for large-scale AI models in modern vehicles. The role involves improving system performance and reducing latency on embedded platforms.

The ideal candidate will have a Master's or PhD in a relevant field, strong expertise in C++ and Python, and experience with performance optimization.

This position offers the chance to work in a dynamic environment that prioritizes innovation in autonomous driving technology.

Qualifications

• Master's or PhD in a relevant field with industry or research experience.
• Strong expertise in C++ and Python for production-quality code.
• In-depth understanding of computer architecture and HPC.
• Proven experience in application performance analysis.

Responsibilities

• Design and optimize software for large-scale AI models.
• Profile and improve inference performance across systems.
• Reduce latency and enhance power efficiency on platforms.
• Deliver production-ready optimizations across the fleet.

Skills

C++

Python

Computer architecture

High-performance computing

Performance optimization

Education

Master's or PhD in CS/CE/EE or equivalent

Tools

eBPF

CUDA

Profiling tools

Job description

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.

Key Responsibilities

• Design and optimize software for deploying large-scale AI models in production vehicles.
• Profile and improve inference performance across compute, memory, and I/O systems.
• Reduce latency and improve power efficiency on embedded automotive platforms.
• Deliver production-ready optimizations that scale reliably across the vehicle fleet.

Basic Qualifications

• Master's or PhD in CS/CE/EE or equivalent, with relevant industry or research experience.
• Strong expertise in C++ and Python, including performance-sensitive, production-quality code.
• In-depth understanding of computer architecture and high-performance computing (memory hierarchy, parallelism, vectorization, scheduling).
• Proven experience in application performance analysis and optimization, using profiling tools to diagnose and resolve bottlenecks.

Preferred Qualifications

• Experience writing and optimizing CPU or CUDA kernels.
• Experience developing performance tooling and instrumentation (e.g., eBPF, perf, custom tracing/profiling frameworks) for production or embedded systems.
• Familiarity with embedded or automotive compute platforms (e.g., NVIDIA Orin, Drive) and their power/thermal constraints.
• Previous experience in the autonomous driving or robotics industry.
• Effective at solving complex problems collaboratively within larger cross-functional teams.

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.