XPENG ML Engineer Interview Guide

Getting ready for a Machine Learning Engineer interview at XPENG? The XPENG Machine Learning Engineer interview process typically spans technical, analytical, and problem-solving question topics, evaluating skills in areas like deep learning model development, large-scale distributed training, AI infrastructure, and real-world deployment for autonomous systems. Interview preparation is especially critical for this role at XPENG, as candidates are expected to demonstrate expertise in designing and optimizing advanced ML models, translating vast multimodal data into actionable insights, and collaborating across research and engineering teams to push the boundaries of intelligent mobility.

In preparing for the interview, you should:

• Understand the core skills necessary for Machine Learning Engineer positions at XPENG.
• Gain insights into XPENG’s Machine Learning Engineer interview structure and process.
• Practice real XPENG Machine Learning Engineer interview questions to sharpen your performance.

At Interview Query, we regularly analyze interview experience data shared by candidates. This guide uses that data to provide an overview of the XPENG Machine Learning Engineer interview process, along with sample questions and preparation tips tailored to help you succeed.

XPENG is a leading smart technology company specializing in electric vehicles (EVs), autonomous driving, robotics, and electric vertical take-off and landing (eVTOL) aircraft. The company is at the forefront of integrating advanced AI, machine learning, and smart connectivity to revolutionize intelligent mobility and reshape the future of transportation. XPENG invests heavily in cutting-edge research and development, with a mission to solve complex challenges in autonomous driving and robotics. As an ML Engineer, you will contribute to developing state-of-the-art machine learning infrastructure and models that power next-generation autonomous vehicles and intelligent systems, directly supporting XPENG’s innovation-driven vision.

As a Machine Learning Engineer at XPENG, you will design, train, and deploy large-scale deep learning models to advance autonomous driving and intelligent mobility technologies. You’ll collaborate with software engineers, research scientists, and cross-functional teams to build state-of-the-art ML infrastructure and optimize model training and inference, leveraging extensive datasets collected from XPENG’s vehicle fleet. Your work may include developing transformer-based architectures, distributed training pipelines, and efficient neural network solutions for real-time applications in vehicles and robotics. This role is pivotal in driving XPENG’s mission to deliver next-generation autonomous transportation and robotics, offering opportunities to work on innovative projects with top talent in the field.

Transitioning from an understanding of XPENG’s mission and the ML Engineer role, let’s break down what you can expect from the interview process and how to strategically prepare for each step.

2.1 Stage 1: Application & Resume Review

The process begins with a thorough screening of your application materials, emphasizing advanced machine learning and deep learning expertise, experience with large-scale model training (especially with frameworks like PyTorch, TensorFlow, and PyTorch Lightning), and a proven track record of deploying models in production environments. XPENG’s technical recruiters and engineering leads look for evidence of hands-on experience with distributed training, transformer architectures, and, where relevant, contributions to autonomous driving or robotics projects. To prepare, ensure your resume clearly highlights your most impactful ML/AI projects, publications (if any), and quantifiable achievements in model optimization, deployment, and scalability.

2.2 Stage 2: Recruiter Screen

A recruiter will reach out for a 30- to 45-minute conversation to discuss your background, motivation for joining XPENG, and alignment with the company’s values and high-level technical needs. Expect to be asked about your interest in intelligent mobility, autonomous systems, and how your experience fits XPENG’s focus areas. Preparation should center on succinctly articulating your ML engineering journey, your passion for AI-driven transportation, and your familiarity with XPENG’s technology stack and mission.

2.3 Stage 3: Technical/Case/Skills Round

This stage typically involves one to two rounds of in-depth technical interviews, conducted virtually or onsite, led by senior ML engineers or technical managers. You’ll be evaluated on your mastery of deep learning fundamentals, distributed model training, system design for large-scale ML infrastructure, and problem-solving skills. Expect to tackle hands-on coding exercises (in Python, PyTorch, or TensorFlow), ML case studies (such as designing a model to predict ride acceptance or building scalable ETL/data pipelines), and theoretical discussions around neural networks, transformers, reinforcement learning, and model optimization (including quantization, latency reduction, and deployment on edge devices). Demonstrating your ability to reason through ambiguous ML problems and communicate your design decisions clearly is key.

2.4 Stage 4: Behavioral Interview

XPENG places a strong emphasis on collaboration, adaptability, and communication. This round, often conducted by engineering managers or cross-functional partners, explores your experience working on large, multidisciplinary teams, your approach to navigating project hurdles, and your ability to translate complex technical insights for diverse audiences. You may be asked about times you mentored colleagues, resolved technical disagreements, or contributed to open-ended innovation. Prepare to share stories that highlight your teamwork, leadership, and impact, especially in high-stakes or fast-paced environments.

2.5 Stage 5: Final/Onsite Round

The final stage typically consists of a series of onsite (or virtual onsite) interviews—often 3 to 5 sessions—covering advanced technical deep-dives, system design, cross-functional collaboration, and culture fit. You’ll meet with senior engineers, technical leads, and sometimes product or research leaders. Expect to discuss end-to-end ML system architecture, present a past project or research (with a focus on scalability, reliability, and business impact), and engage in whiteboarding or live coding. For roles closely tied to autonomous driving or robotics, you may be asked to design solutions for multimodal data integration, real-time inference on embedded hardware, or innovative ML applications in smart mobility.

2.6 Stage 6: Offer & Negotiation

If successful, you’ll receive a formal offer, typically presented by the recruiter or HR team. This stage covers salary, equity, bonus, and benefits, as well as discussions about your anticipated impact and growth trajectory at XPENG. Be prepared to discuss your compensation expectations and clarify any questions about role scope, team structure, and long-term opportunities.

2.7 Average Timeline

The typical XPENG ML Engineer interview process spans 3 to 5 weeks from initial application to offer. Candidates with highly relevant experience or strong referrals may move through the process in as little as 2 to 3 weeks, while the standard pace allows about a week between each round for scheduling and feedback. Technical and onsite rounds may be condensed into a single day or split across several days, depending on candidate and interviewer availability.

Next, let’s explore the specific interview questions you’re likely to encounter throughout the XPENG ML Engineer process.

Below are sample questions you may encounter when interviewing for an ML Engineer position at XPENG. Focus on demonstrating your ability to design robust machine learning systems, communicate technical concepts to diverse audiences, and solve real-world data challenges. Prepare to discuss both your technical depth and your strategic thinking in the context of XPENG’s fast-paced, product-driven environment.

3.1 Machine Learning System Design & Modeling

Expect questions that test your ability to architect and evaluate end-to-end ML solutions, from problem framing to deployment. Emphasize your experience with real-world constraints, scalability, and model interpretability.

3.1.1 Building a model to predict if a driver on Uber will accept a ride request or not
Frame the problem as a binary classification task. Discuss feature selection, data imbalance handling, and how you would evaluate model performance in a production environment.

3.1.2 Identify requirements for a machine learning model that predicts subway transit
Outline how you would gather and preprocess transit data, select features, and define success metrics. Address the challenges of time-series forecasting and real-time inference.

3.1.3 Creating a machine learning model for evaluating a patient's health
Describe your approach to modeling health risk, including data sources, feature engineering, and handling sensitive information. Emphasize how you would validate and monitor the model post-deployment.

3.1.4 Designing an ML system for unsafe content detection
Discuss the architecture for a scalable, real-time content moderation system. Address data labeling, model retraining, and minimizing false positives/negatives.

3.1.5 Let's say that you're designing the TikTok FYP algorithm. How would you build the recommendation engine?
Explain collaborative filtering, content-based methods, and how you’d incorporate user feedback. Address cold-start problems and fairness in recommendations.

3.2 Deep Learning & Model Selection

These questions probe your understanding of neural network architectures, optimization, and when to choose specific algorithms for business problems.

3.2.1 Explain neural nets to kids
Simplify neural networks using analogies. Focus on conveying intuition over jargon, and show you can communicate with non-experts.

3.2.2 When you should consider using Support Vector Machine rather than Deep learning models
Compare SVMs and deep learning in terms of data size, interpretability, and computational resources. Justify your choice based on scenario requirements.

3.2.3 Why would one algorithm generate different success rates with the same dataset?
Discuss data splits, randomness, hyperparameters, and data leakage. Highlight the importance of reproducibility and robust evaluation.

3.2.4 Explain what is unique about the Adam optimization algorithm
Summarize the advantages of Adam over other optimizers, such as adaptive learning rates and momentum. Relate these features to practical training scenarios.

3.2.5 Implement logistic regression from scratch in code
Describe the mathematical foundations and stepwise implementation. Highlight how you’d validate correctness and optimize performance.

3.3 Data Engineering & Pipeline Design

XPENG values scalable, reliable data pipelines for ML. These questions assess your ability to design, optimize, and maintain data flows for large-scale applications.

3.3.1 Design a scalable ETL pipeline for ingesting heterogeneous data from Skyscanner's partners.
Detail the architecture, including data ingestion, transformation, and storage. Address fault tolerance and scalability.

3.3.2 Design an end-to-end data pipeline to process and serve data for predicting bicycle rental volumes.
Lay out the steps from raw data collection to model serving. Emphasize automation, monitoring, and retraining strategies.

3.3.3 Let's say that you're in charge of getting payment data into your internal data warehouse.
Explain your approach to data validation, schema evolution, and ensuring data integrity across multiple sources.

3.3.4 Design a feature store for credit risk ML models and integrate it with SageMaker.
Describe the benefits of a feature store, integration points, and governance strategies for high-quality features.

3.4 Data Analysis, Experimentation & Business Impact

These questions focus on translating data science into business strategy and actionable insights. XPENG looks for engineers who can connect modeling to measurable outcomes.

3.4.1 You work as a data scientist for ride-sharing company. An executive asks how you would evaluate whether a 50% rider discount promotion is a good or bad idea? How would you implement it? What metrics would you track?
Describe designing an A/B test, tracking conversion, retention, and ROI. Discuss how you’d communicate results to stakeholders.

3.4.2 How to present complex data insights with clarity and adaptability tailored to a specific audience
Share strategies for storytelling, visualization, and tailoring technical depth. Stress the importance of actionable recommendations.

3.4.3 Making data-driven insights actionable for those without technical expertise
Discuss methods for simplifying analytics, using analogies, and visual aids. Emphasize bridging the gap between data and decisions.

3.4.4 How would you approach the business and technical implications of deploying a multi-modal generative AI tool for e-commerce content generation, and address its potential biases?
Address both technical deployment and ethical considerations. Discuss bias mitigation, monitoring, and stakeholder education.

3.4.5 Describing a data project and its challenges
Explain a challenging project, your problem-solving process, and how you measured success. Highlight lessons learned and impact.

3.5 Behavioral Questions

3.5.1 Tell me about a time you used data to make a decision.
Describe a situation where your analysis led to a clear business outcome. Focus on your process, how you communicated your findings, and the impact on the organization.

3.5.2 Describe a challenging data project and how you handled it.
Share a complex project, the obstacles you faced, and the steps you took to overcome them. Emphasize adaptability and problem-solving.

3.5.3 How do you handle unclear requirements or ambiguity?
Explain your approach to clarifying objectives, engaging stakeholders, and iterating on solutions. Highlight techniques for reducing risk and uncertainty.

3.5.4 Tell me about a time when your colleagues didn’t agree with your approach. What did you do to bring them into the conversation and address their concerns?
Discuss a disagreement, how you facilitated open dialogue, and the resolution. Show your ability to collaborate and influence without authority.

3.5.5 Share a story where you used data prototypes or wireframes to align stakeholders with very different visions of the final deliverable.
Describe how you bridged the gap between technical and non-technical teams using rapid prototyping. Focus on communication and iterative feedback.

3.5.6 Tell me about a time you delivered critical insights even though 30% of the dataset had nulls. What analytical trade-offs did you make?
Explain your approach to missing data, including profiling, imputation, and communicating uncertainty. Emphasize transparency and business impact.

3.5.7 Describe how you prioritized backlog items when multiple executives marked their requests as “high priority.”
Outline your prioritization framework, stakeholder management, and how you balanced competing demands. Highlight results and lessons learned.

3.5.8 Give an example of automating recurrent data-quality checks so the same dirty-data crisis doesn’t happen again.
Share how you identified a recurring issue, built automation, and measured improvement. Emphasize scalability and reliability.

3.5.9 Tell me about a situation where you had to influence stakeholders without formal authority to adopt a data-driven recommendation.
Describe your persuasion strategy, how you built trust, and the outcome. Focus on data storytelling and business alignment.

3.5.10 Describe a situation where two source systems reported different values for the same metric. How did you decide which one to trust?
Explain your validation process, cross-referencing, and communication with stakeholders. Emphasize integrity and thoroughness.

4.1 Company-specific tips:

Immerse yourself in XPENG’s mission to redefine intelligent mobility through AI, machine learning, and autonomous systems. Study their product portfolio, including electric vehicles, autonomous driving platforms, robotics, and eVTOL aircraft, so you can confidently discuss how your expertise aligns with their vision for next-generation transportation.

Familiarize yourself with XPENG’s approach to AI-driven vehicle technology, especially their investment in large-scale data collection from vehicle fleets, multimodal sensor fusion, and real-time inference on embedded hardware. Be prepared to reference recent advancements or public research initiatives XPENG has undertaken in deep learning, autonomous driving, or smart connectivity.

Understand the business context and technical constraints unique to XPENG, such as safety-critical model deployment, scalability across millions of vehicles, and regulatory considerations in autonomous systems. Demonstrating awareness of these challenges will show you’re ready to contribute meaningfully to their mission.

4.2 Role-specific tips:

4.2.1 Master deep learning fundamentals, especially transformer architectures and distributed training.
XPENG expects ML Engineers to design and optimize advanced neural networks for autonomous vehicles and robotics. Sharpen your knowledge of transformer-based models, attention mechanisms, and large-scale distributed training techniques. Be ready to explain how you’d scale training across multiple GPUs or nodes, and optimize for latency and throughput in real-world applications.

4.2.2 Practice designing robust ML pipelines for multimodal and time-series data.
You’ll often work with data from sensors, cameras, LIDAR, and vehicle telemetry. Prepare to discuss how you would architect ETL pipelines that ingest, preprocess, and serve heterogeneous data at scale. Highlight your experience with feature engineering for multimodal inputs, handling missing or noisy data, and ensuring data integrity for safety-critical ML systems.

4.2.3 Demonstrate your ability to deploy models in production, especially on edge devices.
XPENG values engineers who can take models from research to deployment. Be prepared to discuss your experience with model quantization, pruning, and optimizing inference for embedded hardware. Show that you understand the trade-offs between accuracy, speed, and resource constraints when deploying models in vehicles or robotics.

4.2.4 Show your expertise in model evaluation, monitoring, and retraining strategies.
Autonomous systems require ongoing evaluation and improvement. Explain how you would monitor model performance post-deployment, set up automated retraining pipelines, and address concept drift or changing data distributions. Highlight your experience with A/B testing, anomaly detection, and ensuring model reliability in dynamic environments.

4.2.5 Communicate complex technical concepts with clarity to diverse audiences.
XPENG’s ML Engineers collaborate across research, engineering, and product teams. Practice simplifying deep learning concepts using analogies and visualizations, tailoring your explanations to both technical and non-technical stakeholders. Be ready to present past projects, emphasizing business impact and actionable insights.

4.2.6 Prepare stories of collaboration, adaptability, and innovation in fast-paced environments.
XPENG values engineers who thrive in multidisciplinary teams and can navigate ambiguity. Reflect on experiences where you mentored colleagues, resolved technical disagreements, or drove innovation under tight deadlines. Share examples that highlight your teamwork, leadership, and ability to deliver results in challenging situations.

4.2.7 Be ready to discuss ethical considerations and bias mitigation in ML systems.
Autonomous and generative AI systems can have significant societal impact. Prepare to articulate your approach to identifying and mitigating bias, ensuring fairness, and maintaining transparency in model development. Show that you’re committed to building responsible AI solutions that align with XPENG’s values and regulatory requirements.

5.1 How hard is the XPENG ML Engineer interview?
The XPENG ML Engineer interview is challenging, especially for candidates aiming to work on autonomous driving and large-scale AI systems. You’ll be tested on deep learning fundamentals, distributed model training, ML system design, and your ability to solve real-world problems in intelligent mobility. The process is rigorous but designed to identify engineers who can innovate and collaborate at the highest level.

5.2 How many interview rounds does XPENG have for ML Engineer?
Typically, there are 5 to 6 rounds: application and resume review, recruiter screen, technical/case/skills interviews, behavioral interviews, final onsite (or virtual onsite) sessions, and offer/negotiation. Each stage is tailored to assess specific skills, from technical depth to culture fit.

5.3 Does XPENG ask for take-home assignments for ML Engineer?
XPENG occasionally assigns take-home technical tasks, especially for candidates shortlisted after initial screens. These may involve designing ML solutions, coding exercises in Python or deep learning frameworks, or data pipeline architecture tasks relevant to autonomous systems.

5.4 What skills are required for the XPENG ML Engineer?
You’ll need strong expertise in deep learning (especially transformer architectures), distributed training, model deployment on edge devices, and scalable data pipeline design. Experience with PyTorch, TensorFlow, multimodal data, and real-world ML applications in autonomous driving or robotics is highly valued. Strong communication, collaboration, and ethical awareness in AI are also essential.

5.5 How long does the XPENG ML Engineer hiring process take?
The typical timeline is 3 to 5 weeks from application to offer. Highly relevant candidates or those with referrals may progress faster, while scheduling and feedback cycles can extend the process for others.

5.6 What types of questions are asked in the XPENG ML Engineer interview?
Expect deep dives into ML system design, distributed model training, neural network architectures, real-time inference, and business impact. You’ll face hands-on coding exercises, theoretical discussions, case studies related to autonomous driving, and behavioral questions focused on teamwork and adaptability.

5.7 Does XPENG give feedback after the ML Engineer interview?
XPENG generally provides feedback through recruiters, especially after onsite rounds. While detailed technical feedback is rare, you’ll receive high-level insights on your strengths and areas for improvement.

5.8 What is the acceptance rate for XPENG ML Engineer applicants?
The role is highly competitive, with an estimated acceptance rate of 3–5% for qualified applicants. XPENG seeks candidates who excel in both technical and collaborative dimensions.

5.9 Does XPENG hire remote ML Engineer positions?
Yes, XPENG offers remote opportunities for ML Engineers, particularly for roles focused on AI infrastructure and model development. Some positions may require periodic onsite visits for team integration and collaboration, especially for projects tied to hardware or vehicle platforms.

• Related guides:
  • XPENG interview questions
  • Machine Learning Engineer interview guide
  • Top Machine Learning interview tips
  • How to Become a Machine Learning Engineer in 2025
  • Top 9 Machine Learning Algorithm Interview Questions for 2025
  • Python Machine Learning Interview Questions Guide 2025 — Coding & Concepts
  • Top 17 Machine Learning Case Studies to Look Into Right Now (Updated for 2025)

Ready to ace your XPENG ML Engineer interview? It’s not just about knowing the technical skills—you need to think like a XPENG ML Engineer, solve problems under pressure, and connect your expertise to real business impact. That’s where Interview Query comes in with company-specific learning paths, mock interviews, and curated question banks tailored toward roles at XPENG and similar companies.

With resources like the XPENG ML Engineer Interview Guide and our latest case study practice sets, you’ll get access to real interview questions, detailed walkthroughs, and coaching support designed to boost both your technical skills and domain intuition.

Take the next step—explore more case study questions, try mock interviews, and browse targeted prep materials on Interview Query. Bookmark this guide or share it with peers prepping for similar roles. It could be the difference between applying and offering. You’ve got this!