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ML Engineer interview prep

Based on 6 reports, the Machine Learning Engineer interview at Instacart is heavily software engineering-inclusive, prioritizing algorithmic coding and system design alongside domain-specific expertise. Candidates should expect coding assessments featuring medium-to-hard problems that focus on data structure implementation, interval manipulation, and complex array or hash map transformations. The process is primarily geared toward senior and staff-level roles and includes significant behavioral and domain-knowledge components to evaluate technical depth.

Based on 6 interview reports · 71 questions

L5 · SeniorL6 · Staff

System Design7

·Design a multi-task ranking model for a marketplace where you're jointly optimizing click-through, conversion, and long-
·Design an embedding-based retrieval system for a marketplace with 500k items, where 1000 new items are added daily. Many
·Design an online experimentation platform for a ranking system serving 10M daily users. You need to run 20+ concurrent A

Domain Knowledge57

·Explain multi-task learning architectures for ranking: shared-bottom, MMoE, PLE, and AITM. For each, derive the gradient
·Walk through two-tower retrieval end-to-end. Derive the training objective. What information leakage problems arise when
·Explain position bias in ranking from first principles. Derive the IPW estimator and its variance problem. Compare resul

Behavioral7

·You're the most senior ML engineer on a team of 6. Two mid-level engineers keep proposing overly complex model architect
·You've been asked to lead the technical strategy discussion for your team's next-year ML roadmap. Product wants 5 new fe
·You need to convince a product manager and a VP that an ML system is NOT worth building — a simpler heuristic would achi

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