Interview Strategy

A guide for mid-to-senior (L4/L5) ML Engineer roles, focusing on system design, project deep-dives, and technical strategy.

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The Interview Landscape (Engineer 2/L4)

At the Engineer 2 / Senior level, interviews shift from "What is X?" to "Why X and not Y?".

The Standard Loop:

1. Recruiter Screen: 30 min high-level background.

2. Coding / DSA (1-2 Rounds): LeetCode Medium/Hard. Focus on strings, graphs, and dynamic programming.

3. ML Coding: Implementing an algorithm (e.g., K-Means, LR) or data maniupulation (Pandas/Numpy).

4. ML System Design (The "Closer"): 45-60 min of leader-led design discussion.

5. ML Theory Depth: Probabilistic "what-if" scenarios.

6. Behavioral / Project Deep Dive: Deep dive into 1-2 major projects using the STAR method.

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ML System Design Framework

This is the most critical round. Use this 5-step framework to lead the discussion.

1. Problem Scoping & Requirements

• Goal: What is the business metric? (e.g., Click-Through Rate (CTR), Revenue, Latency).

• Constraints: Max latency (e.g., <100ms), throughput (QPS), scale (millions of users).

• Type: Is this search, recommendation, ranking, or classification?

2. Data & Feature Engineering

• Sources: Logs, user profile, item metadata.

• Labeling: Explicit (ratings) vs. Implicit (clicks, watch time). Handling delay in labels.

• Features: Categorical (one-hot vs. embeddings), Numerical (scaling), Text (BERT/Word2Vec), Temporal (recency).

• Storage: Feature store considerations.

3. Modeling

• Baseline: Start simple (e.g., Logistic Regression or XGBoost).

• Advanced: Deep Learning (e.g., DeepFM for ranking, Transformers).

• Trade-offs: Model size vs. Accuracy vs. Inference time.

4. Evaluation (Offline & Online)

• Offline: AUC, Log-loss, Recall@K, Precision@K, F1.

• Online: A/B Testing, Interleaving.

• Slicing: Check performance for specific user segments or item categories.

5. Deployment & Post-Production

• Serving: Batch inference vs. Real-time API.

• Optimization: Quantization, Pruning, Distillation.

• Monitoring: Data Drift (K-S test), Model Drift, Latency, Error rates.

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Case Study: Ranking YouTube Videos

Interviewer: "Design a system to rank videos on the YouTube home page."

1. Clarify:

   • Multi-stage ranking? Yes, Candidate Generation → Ranking.

   • Objective? Maximize long-term watch time.

2. Candidate Generation:

   • Filter 1B videos to ~500.

   • Use Two-Tower Networks (User Tower, Video Tower) with Dot Product similarity.

3. Ranking (The "Heavy" Model):

   • Goal: Predict the probability of watch time $> X$.

   • Features: User history, Video embeddings, Context (device, time).

   • Label: Continuous watch time or binary "watched > 30s".

4. Online Serving:

   • Use ANN (Approximate Nearest Neighbors) like Faiss for candidate retrieval.

   • Re-rank the top 500 using the heavy DNN.

5. Cold Start:

   • For new videos, use content-based features (title, tags) before they get user interactions.

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Pro-Tips for the Deep Dive

• Explain the "Why": Why did you choose Adam over SGD in your project? (e.g., faster convergence, handles noisy gradients).

• Acknowledge Trade-offs: "We used XGBoost because it was interpretable and handled our tabular data well, even though a MLP might have slightly higher accuracy."

• Focus on Impact: Always mention the % improvement in business metrics, not just ML metrics.

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Study Checklist

• [] LeetCode: ~100-150 Mediums, focus on the "Top 75".

• [] System Design: Read "Designing Data-Intensive Applications" (DDIA) and "Machine Learning System Design" by Chip Huyen.

• [] Projects: Be ready to talk for 20 minutes about your best work, including failures and pivots.