AI Infra Onboard Performance Intern

Xpengmotors; Santa Clara (CA); On-site; USD 100,000 - 130,000; Full time

Benefits offered by this job

Fun and supportive work environment

Infrastructures and computational resources

Opportunity to work on cutting-edge technologies

Competitive compensation package

Food and fun activities

Job summary

Xpengmotors, a leader in smart technology, seeks a software engineer to design and optimize software for AI models in vehicles. This role is pivotal for enhancing performance and efficiency across embedded systems.

The ideal candidate has a Master's or PhD in a relevant field, expertise in C++ and Python, and is skilled in high-performance computing. Enjoy a supportive work environment and competitive compensation while contributing to autonomous driving advancements.

Qualifications

• Master's or PhD in CS/CE/EE or equivalent with relevant experience required.
• Strong coding skills in C++ and Python, especially for performance-sensitive applications.
• In-depth knowledge of computer architecture and high-performance computing.

Responsibilities

• Design and optimize software for deploying AI models in vehicles.
• Enhance performance across compute, memory, and I/O systems.
• Implement optimizations that scale effectively across the vehicle fleet.

Skills

C++

Python

Performance analysis

Application optimization

High-performance computing

Embedded systems

Education

Master's or PhD in Computer Science, Computer Engineering, or Electrical Engineering

Tools

Profiling tools

CUDA

eBPF

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.