An Interview with a FAANG engineer

Watch someone solve the detect fraudulent and scam practices problem in an interview with a FAANG engineer and see the feedback their interviewer left them. Explore this problem and others in our library of interview replays.

Detect Fraud & Scam Content: E6 Machine Learning System Design Interview with a Meta Engineer - YouTube

Interview Summary

Problem type
Detect fraudulent and scam practices
Interview question
You have been tasked to develop a classifier to detect fraudulent and Scam practices at Facebook

Interview Feedback

Feedback about Atomic Parallelogram (the interviewee)
Advance this person to the next round?
Yes
How were their technical skills?
4/4
How was their problem-solving ability?
4/4
What about their communication ability?
4/4

Strengths:

• Structured Approach: The candidate laid out a clear and comprehensive outline at the beginning, addressing data, features, modeling, evaluation, and deployment.
• Feature Engineering Depth: Demonstrated good understanding of relevant features, including user behavior and content-based features.
• Model Awareness: Showcased knowledge of various models and discussed their pros and cons.
• Handling Class Imbalance: Proactively identified class imbalance as a key challenge and suggested appropriate techniques.
• Business Objective Connection: Attempted to connect the solution to business objectives like revenue impact, user trust, and legal considerations.
• Clarifying Questions: Asked relevant initial questions regarding scale, latency, data types, and categories of fraud.
• Deployment Considerations: Discussed different deployment strategies (shadow, canary, A/B testing) and monitoring aspects.

Areas of Improvement:

• Initial Simplicity & Iteration: The proposed solution was quite complex from the outset. Consider starting with a simpler baseline model and then iterating towards complexity.
• Order of Discussion: It would be more logical to discuss model selection and its rationale before detailing very specific features.
• Critical Trade-offs: Did not explicitly ask about or address crucial trade-offs.
• Exploring Baselines/Alternatives: Could have discussed the potential of existing/old models as a baseline.
• Nuance in Feature Design: Some feature ideas could benefit from more nuanced discussion.
• Questioning Problem Constraints: Could have proactively questioned the necessity of a multi-task approach if one was implied.
• Probing on Business Impact: While business objectives were mentioned, further probing on specific engagement metrics could have strengthened the discussion.

Attaching a template that I recommend for ML System Design

1. Clarification
   • Scope & Scale
   • Business Objective/Key Metrics
2. High-Level ML Approach
   • Type of Learning
   • Trade-offs
3. Training Data
   • Data Collection & Logging
   • Data Imbalance
4. Feature Engineering
   • Feature Selection & Transformation
   • Domain-Specific Features
5. Model Development & Training
   • Objective & Loss Function
   • Hyperparameter Tuning
   • Trade-offs
   • Model Bias
6. Evaluation
   • Metrics
   • Debugging & Holdout Sets
7. Deployment
   • Real-Time vs. Batch Processing
   • Monitoring & A/B Testing
   • Continuous Learning

Feedback about Nefarious Shadow (the interviewer)
Would you want to work with this person?
Yes
How excited would you be to work with them?
4/4
How good were the questions?
4/4
How helpful was your interviewer in guiding you to the solution(s)?
4/4

Interview Transcript

Nefarious Shadow: So this will be a machine learning system design interview. The way generally it works is that I want to know a little bit more about you, what's your background, and if you have any interviews coming up and what companies might they be.

Atomic Parallelogram: Yeah, I'll give you a quick background. I have a PhD in physics. I graduated about three years ago and I have my on-site with Meta next Monday.

Nefarious Shadow: So it depends. So if you feel that you are ready, then it would make sense that we just kind of take it like an actual interview and then give you feedback in the end.

Atomic Parallelogram: Yeah, I understand. Maybe we'll try to wait for the end and if I change my mind partway through, I'll let you know.

Nefarious Shadow: Okay. Yeah, that's. That sounds good.

Atomic Parallelogram: So one quick question before I forget. What's the likelihood of each problem, would you say?

Nefarious Shadow: So it really depends on.

Atomic Parallelogram: Got it.

Nefarious Shadow: So yeah, so we can kind of start with one blinder. So. I think. So this is kind of your job, you come as a machine learning engineer and. The first thing that you might want to end up doing is you want to develop something that can pick up, let's say with some confidence that this might be a fraudulent or this might be a scam content...

Business Objectives

• Promote a safer environment for advertisers to improve trust and safety of the users.
• Reduce the need for human annotation to improve efficiency.
• Ensure compliance with legal considerations for harmful content.

Data and Feature Engineering

• User demographics: age, gender, location, interaction behavior.
• Contextual data: device used, time of day.
• Content metadata: text, image, video, hyperlinks.

Model Development

• Utilize models appropriate for classification tasks with techniques for handling class imbalance.
• Discuss deployment strategies, including monitoring for effectiveness and addressing potential biases.

Evaluation and Metrics

• Emphasize recall over precision due to class imbalance.
• Track metrics like harmful impressions and user engagement.

Deployment Strategies

• Utilize shadow deployment and A/B testing to validate model performance before full-scale rollout.