went through the Instacart MLE loop earlier this year. targeting a senior ML engineer role on one of their personalization / recommendation teams. sharing what I saw.
short version: it's a real MLE loop, not a data science loop in disguise. they care about systems, not just models.
rounds: recruiter screen (30 min) phone screen: ML fundamentals + coding (45 min) virtual onsite: 4 rounds
phone screen: coding problem in python, typical array manipulation. then about 15 min of ML questions: how does logistic regression work, what's the bias-variance trade-off, explain a precision-recall trade-off in a business context. not too hard but you need to be clear and precise.
virtual onsite:
ML design: this was the core round. i got: "design a recommendation system for Instacart's 'you might also need' feature." open-ended. they wanted to see: how i frame the problem (what does 'good' look like?), feature engineering choices, model selection and reasoning, offline vs online evaluation metrics, and how i'd handle cold start. this takes about an hour and goes deep. if you have recsys experience, lean into it. if not, read up on collaborative filtering, matrix factorization, and basic two-tower models.
coding: numpy/pandas problem involving feature computation over a large dataset. vectorized operations mattered. they called out loop-heavy solutions.
ML fundamentals: a lot of depth here. how does gradient boosting work at a high level. explain attention mechanism. what's the difference between L1 and L2 regularization and when would you use each. how do you handle class imbalance in a training set. don't just memorize definitions, they'll ask you to apply them.
behavioral: standard stuff. tell me about a model you shipped that didn't perform as expected. what did you learn.
impressions: instacart's ML problems are legitimately interesting. the supply/demand dynamic between shoppers and customers, geographic constraints, real-time availability changes. that context showed up in interviews in a good way. the interviewers clearly work on this stuff.
my offer was in the $280-320k TC range for SF senior, which felt competitive for 2026 but not top-of-market.