Snapchat·Machine Learning Engineer·Technical Phone Screen·Intermediate

Intermediate

Apr 2026

Summary

Snapchat ML engineer screen covering transformer internals. The attention mechanics went fine but positional encoding tripped me up a bit, and the follow-up question exposed the gap pretty clearly.

Questions Asked (2)

Can you explain how self-attention works in a transformer, including the Q, K, V structure and how attention scores are computed?

System DesignTechnical Trade-offs

Author's notes

Walked through Q, K, V and the scaled dot-product formula without too much trouble.

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AI HintsAI Generated

Suggested Approach

Start by defining self-attention as a mechanism that allows each token to attend to all other tokens in the sequence, then walk through the Q, K, V projections and the scaled dot-product attention computation step by step. Finally, connect this to the transformer architecture and discuss practical implications like computational complexity and trade-offs.

Pro tip: Emphasize the intuition behind Q, K, V as a soft dictionary lookup and mention the scaling factor's role in preventing vanishing gradients. Also, briefly touch on how this scales to long sequences and the trade-offs with alternatives like sparse attention, showing you understand real-world constraints.

1. Motivation and Intuition

Explain why self-attention is needed: to capture dependencies regardless of distance, unlike RNNs. Use the analogy of a soft dictionary lookup where each token queries all others.

2. Q, K, V Projections

Describe how each input token embedding is linearly projected into three vectors: Query (what I'm looking for), Key (what I offer), and Value (what I actually communicate). Mention learned weight matrices W_Q, W_K, W_V.

3. Attention Score Computation

Detail the scaled dot-product: compute dot products between Q and all K, scale by sqrt(d_k), apply softmax to get attention weights, then take weighted sum of V. Show the formula: Attention(Q,K,V) = softmax(QK^T / sqrt(d_k)) V.

4. Multi-Head Attention and Parallelism

Explain that multiple attention heads run in parallel, each with its own Q, K, V projections, allowing the model to attend to different representation subspaces. Mention concatenation and final linear projection.

5. Complexity and Trade-offs

Discuss O(n^2) time and memory complexity with respect to sequence length, and how this impacts long sequences. Mention alternatives like sparse attention or Linformer for efficiency, and trade-offs in accuracy vs. speed.

Key Points to Mention

Self-attention allows each token to attend to all positions in the sequence, capturing long-range dependencies.
Q, K, V are learned linear projections of the input embeddings; Q and K determine attention weights, V carries the information.
Scaled dot-product attention: QK^T / sqrt(d_k) followed by softmax and multiplication with V.
The scaling factor sqrt(d_k) prevents softmax saturation and vanishing gradients.
Multi-head attention enables the model to focus on different parts of the sequence simultaneously.
Computational complexity is O(n^2) in sequence length, which is a key trade-off for long sequences.

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

Why does a transformer require positional encoding at all?

System DesignTechnical Trade-offs

Author's notes

This is where I stumbled.

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AI HintsAI Generated

Suggested Approach

Start by explaining that transformers process tokens in parallel, so they lack the inherent sequential order that RNNs have. Then describe how positional encoding injects information about token positions, enabling the model to understand order and relationships. Finally, discuss the trade-offs and why this design choice is crucial for performance.

Pro tip: Mention that positional encodings are added to input embeddings, not concatenated, and that they can be learned or fixed (e.g., sinusoidal). This shows depth and awareness of implementation details.

1. Highlight the parallel nature of transformers

Explain that transformers process all tokens simultaneously, unlike RNNs which process sequentially. This parallelism loses the notion of order.

2. Explain the need for order information

State that many tasks (e.g., language understanding) depend on word order; without positional encoding, the model would treat 'dog bites man' and 'man bites dog' identically.

3. Describe how positional encoding works

Detail that positional encodings are vectors added to input embeddings, providing a unique signature for each position. They can be fixed (sinusoidal) or learned.

4. Discuss the benefits and trade-offs

Mention that this allows the model to capture relative and absolute positions, and that it's a simple yet effective solution. Note that alternative approaches like relative positional encoding exist.

Key Points to Mention

Transformers lack recurrence and convolution, so they have no built-in notion of order.
Positional encodings are added to token embeddings, not concatenated.
Sinusoidal encodings allow extrapolation to longer sequences than seen in training.
Learned positional embeddings are simpler but may not generalize to unseen lengths.
Without positional encoding, the model is permutation-invariant, which is often undesirable.
Relative positional encodings (e.g., in Transformer-XL) address some limitations of absolute encodings.

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