Meta Machine Learning Engineer interview questions

198 Machine Learning Engineers have shared notes from their Meta interviews, with the questions they were asked in each round. That is 8% of the 2,377 Meta interview notes on Screna AI.

198 Machine Learning Engineer interview notes

Meta·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jul 2026
Prepping for a behavioral loop at Meta for an MLE role. Five prompts, all the usual suspects. Nothing technically surprising but the expectation to reflect on what you'd do differently tripped me up more than I expected.
  • Tell me about a project you're proud of and why.
  • Describe a time you had to work with someone who was difficult. How did you handle it?
  • Walk me through a time you were working against an aggressive deadline. What did you do?
  • How have you handled unexpected changes to a project's scope mid-flight?
  • Give an example of a time you mentored or helped a teammate grow.

“I had a solid story ready but kept second-guessing whether it was impressive enough for Meta's bar.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Meta ML engineer coding round, basically one meaty array problem with a follow-up that I did not see coming. The core question was fine but the streaming extension is what really separated people I think.
  • Given an unsorted array of up to a million integers and an integer k, return the k smallest elements in ascending order. Your solution should run in O(n log k) time and use O(k) extra space using a heap. How do you handle duplicates and negative numbers?
  • Follow-up: how would you redesign this for a streaming scenario where integers arrive one at a time and you need to support a getSmallestK() call at any point?

“I jumped straight to a max-heap of size k and walked through it, which was the right call.”

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Meta·Machine Learning Engineer·Onsite - System Design / Architecture

Jul 2026
Meta ML Engineer system design round focused entirely on building a real-time nearby place recommendation system. Pretty comprehensive scope, they wanted the full stack from metrics down to A/B testing, so it was a lot to cover in one session.
  • Design a real-time system that recommends nearby places to users. Walk through your objectives and success metrics, data sources, candidate generation, feature engineering, ranking model, cold-start handling, exploration vs exploitation, spam filtering, privacy constraints, latency requirements, and an A/B testing plan.

“This is basically a full ML system design in one question.”

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Meta·Machine Learning Engineer·Onsite - System Design / Architecture

Jul 2026
Brutal system design round at Meta for an MLE role. The question was basically 'design a full recommendation system from scratch' and they meant it, every layer, no shortcuts.
  • Design an end-to-end large-scale recommendation system for a consumer app's home feed, covering candidate generation, feature stores, real-time signal ingestion, ranking, re-ranking, and online exploration strategies like multi-armed bandits.
  • How would you handle cold start for both new users and new items in a recommendation system?
  • What are your strategies for detecting and mitigating feedback loops and bias in a recommendation system?
  • How would you design an A/B testing framework for a recommendation system where the treatment and control groups can influence each other through shared item pools?
  • What latency and throughput targets would you set for a home feed recommendation system, and how would your architecture enforce them?
  • How would you handle data privacy requirements within a recommendation system, and what changes to the architecture does that introduce?
  • What is your fallback strategy when parts of the recommendation system go down in production?

“I started with candidate generation and worked forward, which felt logical but in hindsight I think they wanted me to anchor on scale constraints first.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Meta ML Engineer interview with a coding question that looked straightforward but had enough edge case surface area to trip you up if you weren't careful. Felt like a technical phone screen vibe, focused purely on the algorithm.
  • Given an m x n integer matrix, check whether every top-left to bottom-right diagonal contains identical values. Solve it in O(mn) time, discuss space trade-offs, and handle edge cases like empty matrices, single row or column, and negative values.

“I jumped straight to the main logic and forgot to ask about negatives until they brought it up themselves, which was a bit embarrassing.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Meta MLE coding round, two algorithm problems back to back. Nothing behavioral, just pure coding. The second one was harder than I expected for the role.
  • Given a nested list that can contain integers or further nested lists, compute the weighted sum where each integer is multiplied by its depth in the nesting (top-level elements start at depth 1).
  • On an m x n grid with buildings, empty land, and obstacles, find the empty cell whose total shortest-path distance to all buildings is minimized. Return that minimum distance, or -1 if no such cell exists.

“Recursive DFS, pretty clean once you see it.”

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Meta·Machine Learning Engineer·Onsite - Behavioral / Leadership

Jul 2026
Behavioral round for an MLE role at Meta, entirely focused on research experience. The whole thing was basically: pick a project you owned, then defend every decision you made for 45 minutes.
  • Walk me through a research project you personally led, including the problem, your hypothesis, and how you ran experiments.
  • What happened when your results didn't match your hypothesis, or when an experiment failed to show what you expected?
  • Describe a time you disagreed with a collaborator or stakeholder about the direction of a project. How did you handle it?
  • How did you decide what to prioritize when you had multiple competing research directions or limited resources?
  • What was the actual impact of the research you described? How did you measure it or communicate it to others?

“This is the core question and they will stay here for a long time.”

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Meta·Machine Learning Engineer·Technical Phone Screen

Jul 2026
Meta ML Engineer interview with a robot grid exploration problem. No map access, just a move/turn/isTarget API and you have to figure out how to traverse the whole reachable space without looping forever. Classic blind search setup but the unknown initial direction adds a wrinkle most people miss.
  • You control a robot in an unknown 2D grid with walls and open cells. Using only move(), turnLeft(), turnRight(), and isTarget(), implement a function that finds the target cell and stops immediately when reached. The grid boundaries and layout are completely hidden from you.

“The unknown initial direction is the part that trips people up.”

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Meta·Machine Learning Engineer·Onsite - System Design / Architecture

Jul 2026
ML system design round at Meta for an MLE role. The whole thing centered on a single meaty problem involving weapon-related content on a marketplace, and they wanted you to go deep on basically every layer of the ML stack.
  • Design a machine learning system to detect illicit firearm listings on a marketplace, or alternatively build a recommendation or pricing system for legal firearm sales. Walk through problem framing, label definition, data sources, feature engineering, model selection (text classifier, image model, or multi-modal), evaluation metrics, deployment, and monitoring with feedback loops.

“This one sprawls in every direction if you let it.”

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Meta·Machine Learning Engineer·Onsite - System Design / Architecture

Jul 2026
ML system design round at Meta for an MLE role. The whole thing was a single deep-dive into recommendation systems, and they really wanted you to go end-to-end, not just sketch a two-tower model and call it done.
  • Design a large-scale recommendation system for a feed or short-video product. Walk through your business objectives and success metrics, how you'd generate candidates, the ranking model and its features, training data and labels, serving architecture and latency constraints, A/B testing strategy, feedback loops, and cold-start handling.

“This is basically the whole interview in one question.”

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