← Back to Directory

XPeng

Large Enterprises

XPeng is a Chinese electric vehicle manufacturer that designs and produces smart EVs equipped with advanced driver-assistance and autonomous driving technologies. The company is known for its focus on in-house software development, connectivity features, and innovations like flying cars and robotics.

6 interview notes · updated Jul 2026

XPeng·Machine Learning Engineer·Technical Phone Screen

Jul 2026
XPeng ML Engineer interview that went pretty deep into classical DP. The main problem was matrix chain multiplication and they kept piling on follow-ups until I was basically rewriting the whole solution live.
  • Given an array of dimensions representing a chain of matrices, compute the minimum number of scalar multiplications needed to multiply them all together. Return both the minimum cost and one valid optimal parenthesization.
  • Can you reduce the space complexity of your solution, and what are the tradeoffs involved?
  • How would your solution change if each multiplication also had a fixed setup cost on top of the scalar multiplication count?
  • Compare bottom-up dynamic programming versus top-down memoization for this problem. When would you prefer one over the other?

“I knew the classic DP solution going in but fumbled the parenthesization reconstruction part.”

View Post

XPeng·Software Engineer·Technical Phone Screen

Jul 2026
XPeng data engineer interview that was basically a deep dive into storage formats and distributed processing trade-offs, framed around autonomous driving infrastructure. No behavioral stuff, just pure technical back-and-forth on Spark, Parquet, Iceberg, Hive, and when to reach for MongoDB.
  • In PySpark, what is the difference between row-wise and column-wise operations, and when would you use each?
  • What is Parquet and why does it show up so often in analytical data pipelines?
  • When would you pick MongoDB or another NoSQL solution over a lakehouse table format like Iceberg?
  • When would Iceberg be the better choice over MongoDB or a traditional Hive table?
  • Why would a company migrate from Hive tables to Iceberg, and what concrete improvements does Iceberg actually deliver?

“I talked through UDFs vs native column expressions and mentioned that row-wise ops are more flexible but kill parallelism if you're not careful.”

View Post

XPeng·Machine Learning Engineer·Technical Phone Screen

May 2026
Technical screen for an ML Engineer role at XPeng, heavy on RL fundamentals and Transformer architecture. The questions went pretty deep, felt more like a research discussion than a standard interview.
  • What are the main advantages of PPO compared to vanilla policy gradients and TRPO?
  • Write out the Bellman equation for value functions and explain how it fits into actor-critic methods.
  • Compare on-policy and off-policy learning in terms of data reuse, stability, and sample efficiency. Where does PPO fall and why?
  • Describe the clipped surrogate objective in PPO. What roles do clipping, entropy bonus, advantage normalization, and GAE each play in keeping training stable?
  • Explain how self-attention works in Transformers, including positional encodings, computational complexity relative to RNNs and CNNs, and when you'd prefer Transformers in RL or sequence modeling.

“I had a decent answer here.”

View Post

XPeng·Software Engineer·Technical Phone Screen

May 2026
XPeng data engineer interview, got a classic LRU cache problem but with a relaxed constraint where they said a linked list wasn't required. Pretty reasonable as far as coding rounds go.
  • Build an in-memory key-value store with a fixed capacity that evicts the least recently used key when full. It needs to support get and put operations, where both accessing and updating a key count as recent use. A hash map plus a plain list is acceptable instead of a linked list.

“I'd done LRU before so the concept wasn't the issue.”

View Post

XPeng·Machine Learning Engineer·Technical Phone Screen

May 2026
Technical phone screen for an ML Engineer role at XPeng, focused almost entirely on one deep PPO implementation question. It was the kind of question where you can tell they actually want to see if you've run the thing end-to-end, not just read a paper about it.
  • Walk through how you implemented PPO end-to-end in a project: environment setup, observation and action spaces, reward shaping, rollout collection, horizon length, and advantage estimation. Then cover your key hyperparameters, normalization strategy, parallelization, checkpointing, evaluation protocol, and how you handled sim-to-real transfer if applicable.

“This was basically one question that kept expanding.”

View Post

XPeng·Machine Learning Engineer·Technical Phone Screen

May 2026
Interviewed for an ML Engineer role at XPeng. Two questions, both pretty standard for this kind of screen, one resume walkthrough and one 'why us' with a forward-looking twist about what you'd actually do in the first year.
  • Walk me through your resume, focusing on projects that are relevant to reinforcement learning or systems engineering.
  • Based on what you know about where the company is headed, why do you want to join, and what would you actually contribute in your first 6 to 12 months?

“I had prepped a general resume walkthrough but the RL angle threw me a bit since my strongest projects lean more toward supervised learning pipelines.”

View Post