XPeng·Machine Learning Engineer·Technical Phone Screen·Senior

Senior

May 2026

Summary

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.

Questions Asked (1)

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.

System DesignTechnical Trade-offsAlgorithms & Data Structures

Author's notes

This was basically one question that kept expanding.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Structure your answer as a chronological narrative of the PPO implementation, starting from environment setup and moving through training to deployment. Emphasize key design decisions and trade-offs, especially those relevant to autonomous driving (e.g., sim-to-real). Use concrete examples and metrics to demonstrate impact.

Pro tip: Quantify the impact of your choices (e.g., 'reward shaping improved sample efficiency by 30%') and be honest about challenges and how you overcame them. This shows maturity and practical experience.

1. Environment Setup and MDP Definition

Describe the environment, observation and action spaces, and reward shaping. Explain how you designed rewards to encourage desired behavior and any challenges in defining them.

2. PPO Implementation Details

Cover rollout collection, horizon length, advantage estimation (e.g., GAE), and key hyperparameters (clip range, learning rate, batch size). Mention normalization strategies for observations and rewards.

3. Training Infrastructure and Scaling

Explain parallelization (e.g., multiple workers), checkpointing strategy, and how you monitored training. Discuss any distributed training or hardware considerations.

4. Evaluation and Iteration

Describe your evaluation protocol, including metrics, validation environments, and how you used results to iterate on hyperparameters or reward design.

5. Sim-to-Real Transfer (if applicable)

If relevant, discuss techniques like domain randomization, system identification, or fine-tuning on real data to bridge the sim-to-real gap.

Key Points to Mention

Reward shaping techniques and their impact on learning
Generalized Advantage Estimation (GAE) and lambda parameter
Hyperparameter tuning process and final values
Observation and reward normalization methods
Parallelization strategy (e.g., vectorized environments, distributed PPO)
Sim-to-real transfer methods (domain randomization, etc.)

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.