Cohere Machine Learning & Data Science interview questions

Triplet loss contrasts an anchor against one negative; modern contrastive losses like InfoNCE contrast against a whole batch at once, which is why CLIP-style training scales and old triplet pipelines stalled. The mining problem, the batch-size lever, and the call interviewers want.

You can't human-review every summary, and an LLM that wrote the summary can't be trusted to grade it. The move is span-level entailment against the transcript, then a threshold tuned to your cost-of-miss, not 0.5.

A static threshold on prompt length leaves money on the table both ways. The router that learns is a contextual bandit, and the interview lives in the reward function and the cold-start handling, not the algorithm name.

Every FDE loop opens with this, but the definitions are table stakes. What gets scored is whether you can map each metric to a dollar cost the customer recognizes, here's the framing that does it.

A 0.95 AUC model can still flood a review queue with garbage. Knowing when ROC AUC flatters a model, and what to report instead, separates FDEs who've shipped from those who've only run notebooks.

This is a discovery question wearing an ML costume. The strongest candidates run a four-step translation from business pain to loss function, and put a number on each error before choosing anything.

This exact gap kills more customer pilots than any modeling choice. There's a ranked list of culprits, and one of them hides inside innocent-looking feature pipelines at almost every enterprise.

A two-minute explanation, then the real test: learning-rate pathologies, why we use mini-batches, and the one practical diagnosis, reading a loss curve, that customer-facing engineers actually need.

The accuracy trap is the easy part. Interviewers keep pushing: resample or reweight? Does SMOTE survive contact with production? What happens to your probabilities? Here's the full chain.

Everyone can describe the five folds. The question is really about the second half, the two data shapes, both everywhere in enterprise data, where standard k-fold quietly lies to you.

The textbook answer gets a polite nod. The version that names which side of the tradeoff a 6-week customer pilot usually dies on gets the hire signal, here's that version.

The empirical answer for tabular data hasn't changed in a decade, but interviewers want the why, and the two situations where the default is wrong. A decision table you can defend under panel Q&A.

"Use regularization" is the answer everyone gives. The ordered toolbox, and which tool to reach for first when a customer's pilot model collapses in week two, is what actually gets scored.

Time-series is where standard ML habits, random splits, fancy models first, single-number forecasts, fail loudest in front of customers. The baseline discipline and backtesting setup that keep you credible.

On enterprise data, the feature pipeline routinely buys 2-5x the lift of a model swap. Here's the worked churn example, and the leakage rule every engineered feature must pass, that makes the argument stick.

Explainability is where ML deals in banking, insurance and healthcare live or die. What SHAP actually computes, the per-decision worked example reviewers expect, and the trap of reading it as causality.

Every RAG deployment runs on this math, and most candidates can't say why cosine beats Euclidean, or when the two are secretly identical. The 90-second version with a worked example.

Unsupervised questions filter out candidates who've only done Kaggle. The elbow method is the warm-up, the real scoring is on validation without labels and the marketing-team handoff.