Google DeepMind MLE Interview Questions (2026)

Free questions with model answers (6)

• #50 Compare greedy, beam, top-k, top-p, and temperature decoding, and identify when each one fails.

  Greedy decoding always takes the largest logit, beam keeps several high-probability sequences, and sampling draws from a temperature-scaled distribution.

• #168 Compare tensor, pipeline, data, sequence, and expert parallelism and explain when to combine them.

  Data parallelism (DP): every GPU holds a full copy of the model and processes a different slice of the batch; the only cross-GPU traffic is an all-reduce of gradients (or parameters) once per step, so its cost scales with model size, not with activations.

• #294 Implement top-k, top-p, and temperature sampling over a logits vector.

  Scale logits by temperature, mask outside top-k or the cumulative top-p set, subtract the maximum for stability, softmax, and sample.

• #707 Moe forward pass with a shared expert

  Key insight: route each token to a few specialist experts, but also run one shared expert on every token.

• #733 Mixture of experts load balancing loss

  Key insight: without a balancing term, gating collapses onto a few experts.

• #735 Adam optimization algorithm

  Key insight: Adam keeps per-parameter first and second moment estimates of the gradient and corrects their initialization bias, giving an adaptive step size per parameter.