Machine Learning Engineer - Ads Role at Nextdoor: Interview Process Overview

As a candidate who has interviewed for the Machine Learning Engineer - Ads position at Nextdoor, I can provide a detailed overview of the interview process, the key areas of focus, and examples of questions you may encounter during the interview.

Role Overview:

The Machine Learning Engineer - Ads role at Nextdoor involves working on machine learning systems that power the platform’s advertising infrastructure. You will be responsible for developing models and algorithms to optimize ad targeting, bidding, and user engagement with advertisements. Additionally, you will work on improving ad relevance, analyzing user behavior, and developing scalable solutions for personalized content.

Interview Process:

The interview process for the Machine Learning Engineer - Ads role typically involves multiple stages to assess both technical and behavioral competencies. Here’s what you can expect:

1. Initial Screening (30-45 minutes)

Focus:
The initial conversation is usually with a recruiter or hiring manager who will review your resume, qualifications, and experience. They will also gauge your interest in the role and discuss your motivation for applying.

Common Questions:

• “Why are you interested in working as a Machine Learning Engineer in the Ads team at Nextdoor?”
• “Can you tell me about your experience with building machine learning models for large-scale data applications?”
• “What makes you excited about working on ads-related ML projects?“

2. Technical Phone Screen (1 hour)

Focus:
This round is usually conducted by an engineering manager or senior data scientist. You will be asked to solve technical problems in real-time, focusing on your understanding of machine learning algorithms, system design, and coding skills.

Key Topics:

• Machine Learning Algorithms: Expect questions on supervised learning algorithms (e.g., linear regression, decision trees, boosting methods like XGBoost, and neural networks).
  Example:
  • “How would you design a recommendation system for personalized ads?”
  • “Explain how a decision tree works and how it is used for classification tasks in ad targeting.”
• Model Evaluation: You might be asked to demonstrate how you would evaluate the performance of a machine learning model in an advertising context.
  Example:
  • “Given a dataset with ad impressions and click-through rates, how would you evaluate the performance of a classification model?”
• Coding Challenge: You might be asked to write a function or solve a problem in an online coding platform or whiteboard session. This could involve algorithms related to search, sorting, or working with data structures like arrays, trees, or graphs.
  Example:
  • “Write a function to calculate the lifetime value of a user in a subscription-based ad service.”

3. Technical Deep Dive (1.5 hours)

Focus:
In this round, the interviewer will ask you to solve more complex technical problems that are closely related to the advertising systems at Nextdoor. You will be expected to demonstrate how you would design scalable machine learning models to optimize ad targeting and user engagement.

Example Questions:

• “Design a system to dynamically optimize keyword bidding in an ad auction. What machine learning models would you use?”
• “How would you handle class imbalance in a dataset when building a model for predicting user interactions with ads?”
• “Explain how you would build a real-time recommendation engine for serving ads to users based on their browsing history and engagement patterns.”
• “Describe the trade-offs between using a collaborative filtering approach vs. a content-based filtering approach for ad recommendations.”

4. Behavioral Interview (45 minutes)

Focus:
This stage evaluates your leadership, teamwork, and communication skills. Since you will be working in a cross-functional team, this interview assesses how well you collaborate with product managers, engineers, and data scientists.

Common Questions:

• “Tell us about a time when you had to balance trade-offs between model complexity and execution speed. How did you decide?”
• “Describe a situation where you disagreed with a teammate about the approach to solving a technical problem. How did you resolve it?”
• “How do you ensure that the machine learning models you develop are explainable and interpretable to stakeholders?”
• “What steps do you take to ensure the ethical use of machine learning models, especially in sensitive areas like ad targeting?“

5. Final Interview (1 hour with Senior Leadership or CTO)

Focus:
This final round will focus on your overall vision for machine learning at scale, your ability to align technical goals with business objectives, and how you would contribute to the broader team culture.

Questions:

• “How do you approach scaling machine learning models to handle millions of users and ads in a real-time system?”
• “In the context of ads, how would you optimize user engagement while ensuring that the models do not reinforce biases?”
• “What are the most important considerations when designing machine learning systems for monetization?”

Key Skills & Areas of Focus:

To succeed in this interview, focus on the following areas:

1. Machine Learning Algorithms

Be well-versed in common algorithms used in ad tech, including classification, regression, ranking models, collaborative filtering, and reinforcement learning.

2. Data Engineering

Be prepared to discuss your experience working with large datasets, data pipelines, and real-time data processing. Familiarity with tools like Hadoop, Spark, and cloud services (AWS, GCP) is helpful.

3. Model Evaluation & Optimization

Understanding how to evaluate model performance in an ad context is essential. You should be familiar with metrics like click-through rate (CTR), conversion rate, and user lifetime value (LTV).

4. Scalability & Productionization

Experience with deploying machine learning models in production environments at scale is critical. Be prepared to discuss model versioning, monitoring, and performance tracking.

5. Ethical AI

Be ready to discuss how you would handle ethical concerns in ad targeting, such as data privacy, bias, and fairness in machine learning models.

Sample Interview Questions:

ML Model Design:

• “How would you build a model that predicts the probability of a user clicking on an ad, using features like user demographics, ad content, and previous interactions?”

Algorithms:

• “What is the difference between a random forest and gradient boosting? Which would you use for predicting ad clicks, and why?”

Case Study:

• “You are tasked with improving the performance of an ad recommendation system. The current model is underperforming. What steps would you take to diagnose and fix the problem?”

Behavioral:

• “Tell us about a time when you worked on a project that required collaboration across multiple teams. How did you ensure success?”