Application Guide
How to Apply for Staff Software Engineer
at Xpeng Motors
🏢 About Xpeng Motors
Xpeng Motors is pioneering intelligent electric vehicles and eVTOLs with a focus on sustainable mobility and cutting-edge autonomy technology. Unlike traditional automakers, Xpeng integrates advanced AI and machine learning directly into vehicle systems, creating a unique environment where software engineers directly impact next-generation transportation. Working here means contributing to eco-friendly innovation while tackling complex distributed systems challenges at scale.
About This Role
This Staff Software Engineer role focuses on architecting distributed systems for autonomy software evaluation and building ML infrastructure to enhance engineering efficiency. You'll design complex workflows on cloud/on-premise infrastructure to assess software quality and leverage LLMs to optimize triaging and troubleshooting processes. This position directly impacts Xpeng's ability to safely deploy autonomous driving features by creating the systems that evaluate and validate their performance.
💡 A Day in the Life
You might start by reviewing distributed system performance metrics for autonomy evaluation pipelines, then collaborate with ML engineers to design infrastructure improvements for their workflows. Afternoon could involve architecting new Kubernetes deployments for hybrid cloud/on-premise ML workloads or implementing LLM-powered tools to automate troubleshooting of software quality issues. The role balances deep technical design with cross-functional collaboration to ensure release readiness of Xpeng's autonomous driving features.
🚀 Application Tools
🎯 Who Xpeng Motors Is Looking For
- Has 10+ years building backend services with deep expertise in C++ for performance-critical systems and Python for ML tooling
- Demonstrates expert-level knowledge of Kubernetes for orchestrating distributed systems and experience with queueing systems (like Kafka or RabbitMQ) and in-memory data structures (Redis, Memcached)
- Possesses 3+ years specifically designing and maintaining production ML infrastructure, including deployment pipelines for both cloud (AWS/GCP/Azure) and on-premise environments
- Has experience implementing modern Python tooling (Ruff, Mypy) in large codebases and practical applications of LLMs for engineering workflow optimization
📝 Tips for Applying to Xpeng Motors
Quantify your experience with distributed systems by mentioning specific scale metrics (e.g., 'designed system handling 10K+ QPS' or 'managed 500+ node K8s cluster')
Highlight any autonomy or automotive industry experience, even if tangential - Xpeng values understanding of safety-critical systems
Prepare concrete examples of how you've used LLMs to improve engineering processes, not just general AI familiarity
Showcase both cloud AND on-premise infrastructure experience, as Xpeng's hybrid approach is specifically mentioned in the requirements
Tailor your resume to emphasize the 3+ years of ML infrastructure work - this is a key differentiator from general backend roles
✉️ What to Emphasize in Your Cover Letter
['Explain your experience with distributed systems for evaluation/validation (not just general backend systems)', 'Describe specific examples of improving ML engineer workflows through tooling and infrastructure', "Connect your background to Xpeng's mission of sustainable transportation and intelligent EVs", 'Mention any experience with safety-critical systems or automotive software development lifecycles']
Generate Cover Letter →🔍 Research Before Applying
To stand out, make sure you've researched:
- → Xpeng's specific autonomy stack (XPILOT) and their recent advancements in intelligent driving
- → The company's eVTOL (flying car) projects and how software validation differs for aerial vs. ground vehicles
- → Xpeng's technical blog posts or conference talks about their ML infrastructure and distributed systems
- → How Xpeng's Chinese engineering culture might influence remote US team collaboration and processes
💬 Prepare for These Interview Topics
Based on this role, you may be asked about:
⚠️ Common Mistakes to Avoid
- Presenting as a generalist backend engineer without emphasizing ML infrastructure specialization
- Focusing only on cloud experience while neglecting on-premise deployment knowledge
- Using generic AI/ML terminology without concrete examples of production system implementation
- Failing to demonstrate understanding of the safety-critical nature of autonomy software evaluation
📅 Application Timeline
This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.
Typical hiring timeline:
Application Review
1-2 weeks
Initial Screening
Phone call or written assessment
Interviews
1-2 rounds, usually virtual
Offer
Congratulations!