← Google Interview Insights

Google·Machine Learning Engineer·Onsite - Multi Round·Senior

Senior
Jun 2026

Summary

Two-part Google MLE interview, both in Google Docs. First part was a deep-dive on one of your ML projects, second part was live coding a Transformer forward pass from scratch. Felt manageable but the coding section had a lot of moving pieces.

Questions Asked (2)

Q1

Walk us through one of your ML projects in depth: what motivated it, how you handled the data, what modeling decisions you made, what the results were, and what you'd do differently now.

Technical Trade-offsSystem Design
Author's notes

This sounds open-ended but they drill hard once you pick your project.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Choose a project where you made significant technical decisions and can clearly articulate the trade-offs. Structure your answer as a narrative: start with the business problem and motivation, then walk through data handling, modeling choices, results, and lessons learned. Emphasize the 'why' behind each decision and how you measured success.

Pro tip: Quantify the impact of your project (e.g., 'improved accuracy by 15%' or 'reduced latency by 30%') and be honest about what didn't work—Google values intellectual honesty and learning from failures.

1. Set the Context and Motivation

Briefly describe the project's goal, why it mattered to the business or users, and your specific role. Keep it concise to leave time for technical depth.

2. Explain Data Handling

Detail how you collected, cleaned, and preprocessed the data. Mention challenges like missing values, imbalance, or scale, and how you addressed them.

3. Discuss Modeling Decisions

Walk through your choice of algorithms, feature engineering, hyperparameter tuning, and evaluation metrics. Explain why you chose certain approaches over alternatives.

4. Present Results and Impact

Share quantitative outcomes (e.g., accuracy, latency, business metrics) and how they compared to baselines. Highlight any deployment or production considerations.

5. Reflect on Lessons and Improvements

Discuss what you would do differently now, such as using a different model, better data pipeline, or addressing technical debt. Show growth and self-awareness.

Key Points to Mention

  • The specific problem and why it was challenging (e.g., scale, ambiguity, constraints)
  • Data preprocessing steps and how you ensured data quality
  • Model selection rationale and trade-offs (e.g., accuracy vs. interpretability, latency vs. performance)
  • Evaluation metrics and how you validated the model (e.g., cross-validation, A/B testing)
  • Quantitative results and business impact
  • What you learned and how you would improve the project now

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.

Q2

Implement a Transformer-style forward pass covering input embeddings, positional encoding, attention blocks with Q/K/V projections and scaled dot-product attention, a feed-forward block with residual connections and layer norm, and a final output projection.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs
Author's notes

A lot to hold in your head at once.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by outlining the overall architecture and data flow, then dive into each component (embeddings, positional encoding, attention, feed-forward, output projection) with clear explanations of their purpose and implementation. Emphasize the mathematical operations and design choices, such as scaling in attention and residual connections, and discuss trade-offs like computational complexity and memory usage.

Pro tip: Demonstrate awareness of practical considerations: mention that layer norm is typically applied before each sub-layer (pre-norm) for training stability, and that attention can be optimized using techniques like multi-head attention and efficient implementations (e.g., FlashAttention).

1. Input Representation

Explain how input tokens are converted to embeddings and combined with positional encodings to inject sequence order information.

2. Attention Mechanism

Describe the Q/K/V projections, scaled dot-product attention, and multi-head attention, including how masking works for autoregressive decoding.

3. Feed-Forward and Residuals

Detail the position-wise feed-forward network, residual connections, and layer normalization, noting their role in training deep networks.

4. Output Projection

Explain how the final hidden states are projected to vocabulary logits, often with weight tying to the input embeddings.

5. Complexity and Trade-offs

Discuss computational complexity (e.g., O(n^2) attention), memory usage, and potential optimizations like sparse attention or caching.

Key Points to Mention

  • Scaled dot-product attention formula: softmax(QK^T / sqrt(d_k)) V
  • Multi-head attention allows the model to attend to information from different representation subspaces
  • Positional encodings can be learned or fixed (e.g., sinusoidal) and are added to input embeddings
  • Residual connections and layer normalization (pre-norm vs post-norm) are crucial for training stability
  • Feed-forward network typically consists of two linear layers with a ReLU activation in between
  • Weight tying between input embeddings and output projection reduces parameters and improves performance

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.