Scale.ai·Machine Learning Engineer·Technical Phone Screen·Senior

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May 2026Remote

Summary

Scale.ai ML engineer coding round done entirely on Google Colab, no IDE, just NumPy and a starter file with some softmax scaffolding already in place. Two-part attention implementation problem, single-head then multi-head. No LLM allowed, which they apparently enforce somehow.

Questions Asked (2)

Implement scaled dot-product attention from scratch using only NumPy, given Q, K, and V matrices. Include support for an optional additive mask applied before the softmax.

Algorithms & Data StructuresTechnical Trade-offs

Author's notes

The formula itself isn't the hard part, it's remembering to divide by sqrt(d_k) before softmax and not after.

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AI HintsAI Generated

Suggested Approach

Start by clearly stating the scaled dot-product attention formula: softmax(QK^T / sqrt(d_k) + mask) V. Then implement it step-by-step in NumPy, handling the optional additive mask by adding it to the scaled scores before softmax, and ensure numerical stability by subtracting the row-wise maximum before exponentiation.

Pro tip: Mention that you subtract the maximum value per row before softmax to prevent overflow, and that the mask should be added as a large negative number (e.g., -1e9) to effectively zero out attention weights. This shows production-level awareness.

1. Clarify inputs and formula

Confirm the shapes of Q, K, V, and the mask, and restate the scaled dot-product attention formula. Mention that d_k is the last dimension of Q and K.

2. Compute scaled scores

Compute the raw attention scores as Q @ K.T, then scale by 1/sqrt(d_k). Explain why scaling is necessary to prevent softmax saturation.

3. Apply optional mask

If a mask is provided, add it to the scaled scores. Note that the mask should be additive and broadcastable, with large negative values for positions to ignore.

4. Numerically stable softmax

Implement softmax along the last axis: subtract the row-wise maximum, exponentiate, and normalize by the sum. This avoids overflow and underflow.

5. Compute weighted sum and verify

Multiply the attention weights by V to get the output. Optionally, test with small random inputs and compare against a reference implementation or check properties like row sums of attention weights equaling 1.

Key Points to Mention

Scaling factor 1/sqrt(d_k) to counteract large dot products
Additive mask applied before softmax, using large negative values for masked positions
Numerical stability via max subtraction in softmax
Shape compatibility and broadcasting rules for Q, K, V, and mask
Computational complexity O(n^2 d) and memory considerations
Potential for using np.einsum for clarity or efficiency

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

Extend your single-head attention to full multi-head attention: split the embedding dimension across h heads, project Q/K/V separately per head, run attention in parallel, then concatenate and apply an output projection.

Algorithms & Data StructuresSystem DesignTechnical Trade-offs

Author's notes

This is where I lost some time.

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AI HintsAI Generated

Suggested Approach

Start by explaining the motivation for multi-head attention: allowing the model to attend to information from different representation subspaces. Then walk through the step-by-step process: linear projections, splitting into heads, parallel attention, concatenation, and output projection. Finally, discuss the trade-offs and implementation details, such as computational complexity and parameter count.

Pro tip: Emphasize that multi-head attention is not just about parallelism but about enabling the model to capture diverse relationships. Mention that the total computational cost is similar to single-head with full dimension, but the representational power increases.

1. Motivation and Intuition

Explain why multi-head attention is used: to allow the model to jointly attend to information from different representation subspaces at different positions.

2. Mathematical Formulation

Describe how the input is projected into queries, keys, and values for each head using learned linear transformations, and how the dimension is split across heads.

3. Parallel Attention Computation

Detail how scaled dot-product attention is computed independently for each head, potentially in parallel, and how the outputs are combined.

4. Concatenation and Output Projection

Explain that the outputs from all heads are concatenated and then linearly projected to produce the final output.

5. Trade-offs and Implementation

Discuss computational complexity, parameter count, and practical considerations like using efficient matrix operations and handling masking.

Key Points to Mention

Splitting the embedding dimension: d_model divided by h heads, each head operates on d_k = d_v = d_model / h.
Separate linear projections for Q, K, V per head, often implemented as a single large matrix multiplication followed by reshaping.
Scaled dot-product attention: softmax(QK^T / sqrt(d_k)) V, computed per head.
Concatenation of head outputs followed by a final linear projection (W^O).
Computational complexity: O(n^2 * d) similar to single-head with full dimension, but with h times more parameters if not careful.
Benefits: increased representational capacity, ability to focus on different positions, and improved performance in practice.

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