The 60-second answer
Normalize embeddings if cosine similarity is intended, build the pairwise similarity matrix, and divide logits by a temperature parameter. Define the positive mapping explicitly and treat the other batch examples as negatives; use cross-entropy over the similarity logits.
Build the answer in this order
Normalize embeddings if cosine similarity is intended, build the pairwise similarity matrix, and divide logits by a temperature parameter.
Define the positive mapping explicitly and treat the other batch examples as negatives; use cross-entropy over the similarity logits.
Decide whether the loss is symmetric across both views and how duplicate/false-negative examples are handled.
Test with identical positive pairs, shuffled labels, very small temperature, and gradients on both embedding branches.
A useful interview mental model
This is the shape of a strong answer—not a script to memorize.
Senior-level signal
- Senior answers discuss batch-size dependence, false negatives, hard-negative mining, and distributed all-gather for cross-worker negatives.
- Explain why temperature changes both probability sharpness and gradient scale.
What the interviewer is really testing
Likely follow-up questions
Common weak-answer patterns
- Ignoring shape, dtype, device, masking, or broadcasting assumptions.
- Using a framework call without explaining the underlying operation.
- Skipping gradient, numerical-stability, and batching checks.