Home/Blog/Interview Prep/Gen AI interview questions: What recruiters are looking for

Gen AI interview questions: What recruiters are looking for

15 min read

Jan 29, 2025

content

From novice to expert: How to master Generative AI skills

What Google and Meta expect from Generative AI engineers

Google interview process: Focus on model efficiency and System Design

Meta interview process: Focus on real-time personalization and scalability

Sample Generative AI interview questions

Question 1: Write a function that calculates the backpropagation gradient for a simple neural network layer.

Question 2: Design a scalable recommendation system for YouTube using Generative AI.

Question 3: How would you implement model quantization to improve the inference speed of a GPT-based chatbot?

Question 4: Explain the differences between transformers and convolutional neural networks (CNNs).

Question 5: How would you use Generative AI to create a new product feature on Google Photos that automatically generates captions for user-uploaded images?

Question 6: How would you handle an imbalanced dataset when training a large language model?

Question 7: What strategies would you use to fine-tune a large pre-trained model like BERT for a sentiment analysis task on Instagram comments?

Question 8: Implement a basic GAN model in PyTorch.

Question 9: Implement a function to tokenize a sentence using the Hugging Face tokenizer for BERT.

Question 10: Explain the self-attention mechanism and why it's important in transformer models.

Break into Generative AI roles

With demand for Generative AI engineers soaring—LinkedIn reported a 300% increase in AI-related job postings in 2023—tech giants like Google and Meta are racing to hire top talent. These companies aren’t just looking for engineers who can guide LLMs; they need developers who can transform cutting-edge AI research into scalable, real-world applications for millions.

Knowing how to guide LLMs isn't enough—you'll need to master neural network optimization, scalable machine learning pipelines, and production-level deployments.

So what does it take to succeed in one of tech's most competitive fields?

In this blog, we'll break down exactly what FAANG companies expect from Generative AI candidates, including the skills, System Design knowledge, and coding expertise needed to pass their toughest interviews.

We'll cover:

A Generative AI skills roadmap from novice to expert
Google’s interview process, explained
How to stand out in Meta’s interview process
10 sample interview questions based on real-world AI problems
Next steps to break into top AI roles

But what does it take to excel in one of tech’s most competitive fields? Let’s break it down step by step.

“Artificial intelligence is the new electricity.” —Andrew Ng

From novice to expert: How to master Generative AI skills#

If you're looking to build expertise in Generative AI, follow this progression:

Beginner:

Learn the basics: Build a foundation in Python programming, linear algebra, and neutral networks.
Master core machine learning concepts: Study ML fundamentals, including probability theory and algorithms.
Explore key tools: Get hands-on experience with frameworks like PyTorch and TensorFlow.

Intermediate:

Specialize in AI models: Focus on transformers, GANs, and NLP techniques.
Practice with pre-trained models: Work with models like GPT-4 and fine-tune them for specific tasks.
Learn prompt engineering: Optimize responses by mastering prompt writing and model interaction.

Advanced:

Tackle real-world challenges: Apply your skills in Retrieval-Augmented Generation (RAG), transfer learning, and multi-agent systems.
Design scalable AI systems: Create complex architectures that support large-scale AI applications.
Contribute to AI innovation: Stay at the cutting edge by working on research-driven projects and product deployment.

This roadmap will help you begin building comprehensive skills for success in Generative AI, equipping you for impactful roles in the field. But mastering the technical side is only part of the journey.

To land a top role, you'll need to navigate rigorous interviews where companies evaluate not only your technical expertise but also your problem-solving, creativity, and ability to design scalable AI systems.

Next, we'll explore how to prepare for interviews at Google and Meta, breaking down their expectations and processes step by step so you know exactly where to focus your interview prep.

“The best way to predict the future is to invent it.” —Alan Kay

What Google and Meta expect from Generative AI engineers#

Google and Meta both prioritize hiring elite Generative AI talent, but they focus on different core challenges:

Google: Prioritizes large-scale model efficiency, inference speed, and system design for generative models.
Meta: Focuses on real-time personalization, large-scale data handling, and delivering AI-driven user experiences across billions of users.

Let's dive deeper into how that plays out in practice.

Google interview process: Focus on model efficiency and System Design#

Phone screen:
- Goal: To evaluate coding and machine learning fundamentals.
Technical screen expectations, by role:
- Machine Learning Engineer (MLE) / Software Engineer: Candidates in these roles can expect challenges focusing on:
  - Coding: Tasks involving data structures, algorithms, and neural network implementation.
  - Optimization: Model training optimization problems, such as hyperparameter tuning or gradient descent enhancements.
- Data Scientist: The emphasis is on:
  - Data-centric tasks: Implementing statistical algorithms, preprocessing datasets, and working with machine learning libraries.
  - Model development: Leveraging tools like Pandas, NumPy, or scikit-learn to analyze and model data effectively.
- Machine Learning Architect: Interviews test the ability to:
  - Design architectures: Create scalable and efficient model architectures.
  - Optimize systems: Focus on model parallelism, distributed training, and deployment strategies for large AI systems.
- AI Research Scientist: Expect advanced questions on:
  - Models and algorithms: Topics like transformers, GANs, VAEs, and custom generative models.
  - Theoretical AI concepts: Questions on theoretical underpinnings and custom algorithm development.
Coding interviews:
- In this phase, candidates—especially Software Engineers and Machine Learning Engineers—can expect challenges in algorithms and data structures such as dynamic programming, graph traversal, and sorting. AI-specific tasks may include implementing neural networks, probabilistic algorithms, or transformers. Google values speed and clarity in solutions.
- Data Scientists should focus on data manipulation, statistical algorithms, and ML libraries. While less emphasis is placed on traditional algorithms, a strong coding foundation remains essential.

Tip: Google values speed and clarity in your solutions.

System Design interviews
- Expectations for senior-level candidates (AI Researcher, ML Architect) include:
  - Building scalable AI systems: Tasks like designing large-scale inference systems (e.g., GPT-4).
  - Optimization: Distributed training management, low-latency applications, and efficient deployment strategies.
  - Key concepts: Model parallelism, quantization, and system efficiency.
MLE and Data Scientist System Design challenges
- Interviews may focus on:
  - Model deployment: Strategies for efficient inference and training at scale.
  - Performance optimization: Handling data imbalance, memory bottlenecks, and latency issues.
  - Real-time applications: Techniques to optimize speed for live AI solutions.
Domain-specific knowledge: Search and recommendation algorithms
- Candidates may encounter questions on:
  - Generative models: How to integrate models to improve search quality and personalize recommendations.
  - AI-driven techniques: Tackling problems like content filtering, ad personalization, or user behavior prediction.
- This area is especially relevant for Data Scientists and AI Researchers working on domain-focused solutions.

Google doesn’t have a dedicated Data Engineer role; instead, Data Engineering tasks are integrated into MLE and Data Scientist roles. Candidates interested in this field should focus on these positions to gain relevant experience.

Google’s multi-stage interview process for Generative AI roles evaluates technical expertise and AI innovation potential, identifying the most skilled and adaptable candidates.

Let's see how Meta's interview process differs, and what candidates should focus on.

Meta interview process: Focus on real-time personalization and scalability#

Meta’s interview process has a slightly different flavor, focusing on AI at scale, emphasizing real-time personalization across platforms like Facebook, Instagram, and WhatsApp. Engineers must design and optimize AI systems that manage massive datasets while delivering instant results.

Phone screen:
- Goal: Evaluate a candidate’s experience with large-scale AI systems and real-time data processing for Meta platforms.
Technical screen expectations, by role:
- MLE / Data Engineer candidates can expect questions on data preprocessing, feature engineering, distributed ML systems, and optimizing large datasets.
- Data Scientist candidates may face SQL-heavy tasks like complex joins, subqueries, and data aggregation. Candidates should also understand model evaluation, statistical tests, and data preprocessing.
- Software Engineers interviews focus on core coding skills in data structures and algorithms, with AI-related optimizations. Simple System Design questions may cover integrating AI models into software systems.
- Machine Learning Architect candidates may be asked to design end-to-end ML systems for tasks like real-time recommendations, user-generated content processing, and high-speed data pipelines.
System Design: Meta’s System Design interviews emphasize real-time data processing and scalable AI models. Candidates may design systems managing massive user-generated content (e.g., Instagram filters, Facebook recommendations), ensuring low-latency personalized outputs.
- Machine Learning Engineers: System design interviews for Machine Learning Engineers focus on creating scalable ML systems. Candidates may be asked to design systems for data ingestion, preprocessing, training pipelines, or real-time serving of ML models.
- Machine Learning Architect: The design questions here are broader and more complex. Architects are expected to demonstrate an understanding of both software architecture and ML principles.
- AI / Data Scientist: Focus on data-driven decision-making, A/B testing frameworks, data storage, and scalable analytics.
- Data Engineer: Develop high-speed, scalable data pipelines to support Meta's massive data processing needs.
Machine learning pipeline: Meta requires robust, end-to-end ML pipelines from data collection and preprocessing to model deployment and monitoring. Candidates should be familiar with big data tools like Apache Spark and Kafka, as well as low-latency model serving techniques for scalable AI systems.
- Machine Learning Engineers / Architects: Focus on feature engineering, distributed training, and designing scalable, reliable, and cross-platform AI systems.
- AI Research Scientists: Emphasize experimentation pipelines, supporting rapid iterations with minimal resource requirements.
- Data Engineers / Scientists: Specialize in data ingestion, feature engineering, and data transformation for large-scale model training while ensuring high data quality.

Both Google and Meta emphasize AI model scalability, but with distinct focuses: Meta prioritizes real-time personalization and massive data handling, while Google concentrates on system design and inference efficiency for generative models.

Below are a few sample interview questions that will help you prepare for both Google and Meta's rigorous interview processes.

Sample Generative AI interview questions#

Here are some sample Generative AI interview questions to prepare you for the interview.

Question 1: Write a function that calculates the backpropagation gradient for a simple neural network layer.#

import numpy as np
def backprop_gradient(X, W, y_true, y_pred, learning_rate=0.01):
    # Error (loss) calculation (Mean Squared Error)
    error = y_pred - y_true
    
    # Compute the gradient with respect to weights
    gradient = np.dot(X.T, error) / X.shape[0]
    
    # Update weights using gradient descent
    W_new = W - learning_rate * gradient
    
    return W_new, gradient
# Function to simulate forward pass (just a simple linear model)
def forward_pass(X, W):
    return np.dot(X, W)
# Running example
if __name__ == "__main__":
    # Example data (2 features, 3 samples)
    X = np.array([[1.0, 2.0], 
                  [2.0, 3.0], 
                  [3.0, 4.0]])
    
    # Example weights (2 input features -> 1 output)
    W = np.array([[0.5], 
                  [0.3]])
    
    # True output labels
    y_true = np.array([[1.0], 
                       [1.5], 
                       [2.0]])
    
    # Forward pass (calculate predicted values)
    y_pred = forward_pass(X, W)
    
    # Perform backpropagation and update weights
    W_new, gradient = backprop_gradient(X, W, y_true, y_pred, learning_rate=0.01)
    
    # Print updated weights and gradient
    print("Updated Weights:\n", W_new)
    print("Gradient:\n", gradient)

This function computes the gradient of a simple fully connected neural network’s loss function using backpropagation. You will often be asked to implement such functions in interviews where you need to demonstrate your knowledge of the core building blocks of neural networks.

Question 2: Design a scalable recommendation system for YouTube using Generative AI.#

To build such a system, the key is combining traditional recommendation methods with more advanced AI-driven techniques to ensure scalability, reliability, and personalization.

Architectural design: Start by discussing the architecture of a recommendation system that uses collaborative filtering and content-based filtering.

Generate embeddings: Use a pre-trained model (like GPT or BERT) to generate content embeddings from the metadata and comments.

RAG integration: Integrate a RAG framework to refine recommendations.

Building scalable pipeline: Build a scalable pipeline using distributed systems (Apache Spark, Google Cloud), and cache embeddings using vector databases like Chroma database for fast similarity search.

Real-time adaption: Use reinforcement algorithms to adapt recommendations in real-time based on user feedback and interactions.

Question 3: How would you implement model quantization to improve the inference speed of a GPT-based chatbot?#

Quantization reduces the precision of the model weights, typically from 32-bit floating points to 8-bit integers. The steps are as follows:

Convert the trained GPT model to a quantized version using TensorFlow Lite or PyTorch’s quantization tools.
Test the accuracy impact of quantization to ensure the model’s performance does not degrade significantly.
Integrate the quantized model into the inference pipeline to reduce the latency of response generation.

Question 5: How would you use Generative AI to create a new product feature on Google Photos that automatically generates captions for user-uploaded images?#

To answer this, we need to consider how Generative AI can use both image and text processing to create an automatic captioning feature. Combining advanced vision models and natural language processing is key to building such a feature.

Data collection: Use a labeled dataset of images and their respective captions. These pairs provide the necessary context for the model to learn how to generate captions from visual features.
Model architecture: Fine-tune a Vision Transformer (ViT) combined with a GPT model for image caption generation. The ViT can handle image feature extraction, while the GPT model can generate coherent captions based on the extracted image features.

Note: GPT models cannot directly extract image features. ViT is used to process the image and extract a vector representation, also known as embeddings. The extracted features are then input to a model like GPT (or a similar text-generating model) to generate a textual caption describing the image.

Training: Train the model using image-caption pairs with teacher forcing to speed up the learning process.

Note: Teacher forcing involves providing the model with the true output from the previous time step as input for the next time step during training, rather than using the model's own predictions. This helps the model learn faster and more effectively by keeping it on the correct path during the learning process.

Inference: Deploy the model using a cloud-based infrastructure optimized for quick response times (e.g., using TensorFlow Lite for mobile app integration).

Question 6: How would you handle an imbalanced dataset when training a large language model?#

To handle class imbalance in machine learning, various techniques can be applied to ensure the model doesn't favor majority classes. These include data augmentation, weighted loss functions, sampling methods, and using appropriate evaluation metrics.

Data augmentation: Use synthetic data generation to balance underrepresented classes and improve diversity in the dataset.
Weighted loss function: Adjust the loss function to give higher weights to the minority class.
Sampling methods: Apply over-sampling to the minority class or under-sampling to the majority class.
Evaluation: Use metrics like F1-score or AUC-ROC to assess the model's performance, focusing on handling class imbalance.

Question 7: What strategies would you use to fine-tune a large pre-trained model like BERT for a sentiment analysis task on Instagram comments?#

Let’s briefly go over the main steps to fine-tune a large pre-trained model like BERT for sentiment analysis, particularly for Instagram comments, where casual language, emojis, and slang are prevalent.

Data collection: Collect a labeled dataset of Instagram comments and their respective sentiments (positive, negative, neutral).
Preprocessing: Since Instagram comments often include emojis, slang, and hashtags, you'll need to ensure the BERT tokenizer properly handles them. Emojis and hashtags might need to be converted into textual representations, while slang may require expansion into formal language.
Fine-tuning: Use the pre-trained BERT model and fine-tune it on your dataset for the classification task.
Regularization: Apply dropout or L2 regularization to prevent overfitting on the small dataset.
Evaluation: Use cross-validation and F1-score metrics to assess model performance.

For a more detailed discussion on Google Bert, you can take a look the following course:

Getting Started with Google BERT

This comprehensive course dives into Google’s BERT architecture, exploring its revolutionary role in natural language processing (NLP). Starting with BERT’s architecture and pre-training methods, you’ll uncover the mechanics of transformers, including encoder-decoder components and self-attention mechanisms. Gain hands-on experience fine-tuning BERT for NLP tasks like sentiment analysis, question-answering, and named entity recognition. Discover BERT variants such as ALBERT, RoBERTa, and DistilBERT alongside domain-specific adaptations like ClinicalBERT and BioBERT. Explore applications in text summarization, multilingual tasks, and advanced models like VideoBERT and BART. With practical coding exercises and quizzes, you’ll master embeddings, tokenization, and BERT libraries, equipping you to build cutting-edge NLP solutions. Whether you’re new to Google BERT or enhancing your expertise, this course is your guide to state-of-the-art NLP innovations.

25hrs

Intermediate

26 Playgrounds

9 Quizzes

The Generator class creates fake data (e.g., images) from random noise. self.fc is a fully connected layer that converts a 100-dimensional random noise vector into a 784-dimensional vector (used to represent a 28x28 image). The forward() method passes the noise through the fully connected layer and applies a tanh activation to scale the output between -1 and 1 (useful for generating image data).

The Discriminator class determines whether an input image is real or fake. self.fc here is a fully connected layer that converts a 784-dimensional image (flattened) into a single output. The forward() method passes the input through the layer and applies a sigmoid activation to output a probability between 0 and 1, indicating whether the image is real or fake.

For a more detailed version with convolutional layers, non-linear activations, and optimizers to train the GAN, you can check out the following course:

Hands-On Generative Adversarial Networks with PyTorch

Generative adversarial networks (GANs) are machine learning models that generate data resembling a given dataset. GANs have two neural networks: the generator and the discriminator. PyTorch is a popular deep learning framework that is efficient for GAN implementation due to its dynamic computation capabilities. The course begins with what are GANs, activation functions, and model training best practices. You’ll build your first GAN with PyTorch, exploring DCGANs and conditional GANs. Then, you’ll learn image generation with label info, image-to-image translation with pix2pix and CycleGAN, and image restoration techniques. The course concludes with text-to-image synthesis, sequence synthesis, and 3D model reconstruction, providing a comprehensive understanding of GANs. This course equips developers with advanced GAN and DL skills. Mastering GANs using PyTorch will enable you to tackle real-world challenges in various domains like image processing and multimedia content generation.

16hrs

Advanced

29 Playgrounds

10 Quizzes

BertTokenizer is a pre-trained tokenizer specifically designed for BERT models. The model 'bert-base-uncased' is used here, which converts all input text to lowercase before tokenizing.

return_tensors='pt' ensures the output is returned as PyTorch tensors ('pt'), which is suitable for feeding into PyTorch-based models like BERT.

Question 10: Explain the self-attention mechanism and why it's important in transformer models.#

The self-attention mechanism allows a model to focus on different parts of an input sequence when generating each word in an output. It works by computing attention scores that measure how much influence one word in the sequence should have over another. This mechanism enables transformers to capture long-range dependencies in sequences, making them effective for tasks like machine translation or text summarization, where understanding context across the entire input is essential. Unlike RNNs, which process data sequentially, self-attention processes all tokens in parallel, leading to more efficient training.

Break into Generative AI roles#

The field of Generative AI offers incredible opportunities for engineers ready to push the boundaries of innovation. By mastering key skills, designing real-world projects, and preparing rigorously for technical interviews, you can position yourself as a standout candidate at Google, Meta, or beyond.Take the first step today—whether it’s exploring our curated courses, building your first scalable ML pipeline, or solving real-world AI challenges.

With rigorous interview prep—covering both coding and System Design—you’ll be ready to tackle interviews at Google, Meta, and beyond.

Educative gives you access to top-tier courses, from foundational graph traversal techniques to advanced AI System Design. Explore our curated resources and start building the skills that will set you apart in the competitive AI job market.

Written By:

Kamran Lodhi

Join 2.5 million developers at

Explore the catalog

Free Resources

Learn in-demand tech skills in half the time

PRODUCTS

Mock Interview

New

Courses

Skill Paths

Projects

Assessments