XPeng interview questions

Showing 1–5 of 5 results

• Build a Least-Recently-Used StoreMedium

  This question evaluates understanding of caching and eviction policies and practical proficiency with data structures such as hash tables and ordered collections for tracking and updating recency.

  XPengData EngineerCoding & AlgorithmsTechnical Screen
• Design and train a PPO pipelineHard

  This question evaluates proficiency in designing and training Proximal Policy Optimization (PPO) pipelines, covering environment interfacing, observation/action design, reward shaping, rollout and advantage estimation, hyperparameterization, normalization, parallelization, evaluation, and sim‑to‑real considerations.

  XPengMachine Learning EngineerML System DesignTechnical Screen
• Discuss resume and company fitMedium

  This question evaluates a candidate's competence in reinforcement learning and production-grade ML systems, focusing on technical decision-making, measurable project impact, communication of role and motivation, and leadership in planning within the Behavioral & Leadership domain.

  XPengMachine Learning EngineerBehavioral & LeadershipTechnical Screen
• Explain PPO and Transformer basicsHard

  This question evaluates mastery of reinforcement learning algorithms (PPO, policy gradients, TRPO), value-function theory and actor–critic methods (Bellman equations), on- versus off-policy trade-offs and training-stability techniques (clipping, entropy, advantage estimation), plus sequence-modeling fundamentals with Transformers (self-attention, positional encodings and complexity comparisons). It is commonly asked to judge reasoning about algorithmic trade-offs, sample efficiency and stability in practical ML systems, and falls under the Machine Learning domain—specifically reinforcement learning and deep learning/sequence modeling—testing both conceptual understanding and practical application.

  XPengMachine Learning EngineerMachine LearningTechnical Screen
• Implement Stable SoftmaxMedium

  This question tests the ability to implement a numerically stable softmax function by handling potential floating-point overflows or underflows common in high-dimensional vector exponentiation.

  XPengSoftware EngineerCoding & AlgorithmsTechnical Screen