Google Machine Learning Engineer Interview Questions
41+ questions from real Google Machine Learning Engineer interviews, reported by candidates.
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Google onsite
This onsite interview includes five rounds (late Oct) and I failed at the hire committee. The position is a machine learning software engineer. After first round behavior questions, which were...
Hi Everyone, I wanted to share an extremely disappointing experience that happened in my Google L5 onsites. In 2025 beginning I interviewed for SWE, ML role at Google. My initial rounds were two domai
* Location: US * Based on interview timing, interviewer may be from UK/EU/US. * Level: L5 * Status: Cleared 3 coding rounds. Recruiter informed all are strong hire rating. (finally) This is my 3rd att
Google Offer Timeline
Hi everyone, saw there weren't many posts about Google interview timelines. Throughout my interview process I wished there were more timelines so I could refer to them and get an estimate on how long
Hi everyone! Has anyone interviewed for Software Engineer, AI/ML (Sunnyvale/Kirkland) position at Google? I recently got an email saying they are moving forward for following interviews: AI Depth \[Te
Advice for Google coding round (for PhD Software Engineer)
I applied to the following position at Google [Software Engineer, PhD, AI](https://www.google.com/about/careers/applications/jobs/results/122258040807137990-software-engineer-phd-early-career-aimachin
* Location: US * Based on interview timing, interviewer may be from UK/EU. * Level: L5 * Status so far: Cleared 3 coding rounds. Recruiter informed all are positive rating. (finally) This is my 3rd at
**Phone Screening** * **Problem:** Calculate the minimum time required to complete $M$ tasks using $N$ CPUs, where each CPU executes one task at a time without cooldowns. * **Follow-up:** Given the ca
Google SWE-2 (Software Engineer) Interview Experience
**Recruitment Process** The process for the SWE-2 (AIML) role began with a recruiter screening regarding coding preferences, problem-solving history, and current tech stack. This was followed by a res
Google L4 Final Round Interview US
First Round Experience: https://leetcode.com/discuss/interview-question/6271208/google-l4-technical-screen-us/2811861 My interviews has been splitted into 2 days. I did one interview today. I will do the rest (3) tomorrow Question You are given a list of sentences. Your...
Interview Questions for GenAI SDE 3
What kind of question we can expect here ? The interviewer told that round will be on - \[AI Design, ML Core, Model Training\] with a GenAI Manager. Hence, I think it'll not be a purely ML Design roun
Google PhD SWE (ML) reject
Bar must be high I guess. First post of this kind, please be gentle lol. Preparation: Neetcode 150, Jeff H. Sipe. Was comfortable with bfs, dfs, and from this sub/ mock interviews (both google officia
Hi guys, I am just looking to see if anyone has any thoughts on the interview process for Research Scientist positions at meta. For context, I am a 5th year PhD candidate in Biostatistics and have bee
Google SWE III AI/Ml, GenAI, Search Interview Advice
Hi I have upcoming interviews at Google for SWE III ML role in few weeks. I have 2 rounds of interviews: Round 1: Ml domain interview and Googlyness interview (virtual) Round 2: Coding interviews 2 (o
Hey guys ! just posting my exp to see if this is normal. So recruiter contacts me for an SWE-ML position in India during Nov\'23. She scheduled my phone screening interview...
Google | SWE III (L4) ML | Hyderabad | Mar 2024 [Offer]
There aren\'t a lot of Google SWE ML posts here so I thought I\'d contribute. First Contact Recruiter contacted me over email. This was followed by a 15 minute phone call...
Google - Software Engineer III - Machine Learning
Process started with phone screening round + proper 4 rounds of technical interviews. 1) Phone Screening 2) 2 Rounds of DS/Algo questions 3) ML Round 4) Googlyness After phone screening, second round was conducted Question N...
Google SWE III (ML) interview experience (REJECTED)
I recently had received rejection mail from Google for SWE III (ML). Here is my interview experience: Rounds: 1. 3 coding round (DSA) 2. 1 ML domain round 3. 1 googlyness I had first call...
I went through full interview loop at Google for L6 MLE position. This is how it went. I won\'t be able to share the exact questions. Round1: ML system design, went...
Google Rejected me. But the feedback gave me hope.
About a month ago a Google recruiter reached out to me about an ML SWE position and I agreed to interview. Although I wasn't expecting much. With over 800 applications and dozens of interviews and rej
What Google Looks for in Machine Learning Engineer Interviews
Google Machine Learning Engineer interviews are calibrated against the level and scope expected of the role. Across 41+ verified candidate reports on LeakCode, the consistent signals interviewers look for: clear problem decomposition before coding, explicit complexity reasoning, structured handling of edge cases, and the ability to articulate trade-offs between two reasonable approaches.
The discriminator between candidates who advance and candidates who do not is rarely the final correctness of the solution. It is the path to the solution: did you ask clarifying questions, did you state your approach before coding, did you handle edge cases without prompting, and did you communicate your reasoning throughout. Reports tagged "no hire" frequently cite a working solution with poor communication; reports tagged "strong hire" cite clear thinking even when the final solution was incomplete.
How To Use This Question Set
Real interview reports are a calibration tool, not a memorization target. Companies update their question pools every 2-4 months; memorizing exact problems risks misleading you when the interviewer uses a variant. The high-leverage use: identify the patterns that appear repeatedly in Google Machine Learning Engineer reports, practice those patterns on similar (not identical) problems, and use the reports to understand the interviewer's typical follow-up depth.
Filter the questions below by round type, difficulty, and recency. Focus first on reports from the past 6-12 months; older reports may reference questions that have since rotated out of Google's pool. Reports tagged with quantified difficulty (e.g., "medium-hard") are higher-signal than reports without difficulty tags.
Round-by-Round Expectations
Google Machine Learning Engineer loops typically span 4-6 rounds across phone screens and on-site or virtual on-site interviews. The structure varies by company: some run 1 recruiter screen + 1 technical phone + 3-4 on-site rounds; others run 1 recruiter screen + 1 OA + 4-5 on-site rounds. The recruiter screen is logistics and culture-light; the technical phone screen is medium-difficulty coding; the on-site loop covers coding, system design (at L4+ levels), and behavioral rounds.
Each round is designed to surface a specific signal. Coding rounds: correctness, code quality, complexity reasoning, communication. System design rounds: requirements clarification, design judgment, operational thinking. Behavioral rounds: ownership scope, leadership, ambiguity tolerance, conflict navigation. Strong candidates explicitly hit each signal dimension out loud during the round; weak candidates focus only on solving the prompt.
Common Interview Mistakes At This Combination
Reports tagged "no hire" at Google Machine Learning Engineer commonly cite: jumping into code without clarifying requirements, coding silently for 10+ minutes without verbalizing approach, missing edge cases (empty input, single element, very large input, overflow), and producing a working solution that the candidate cannot explain or refactor when probed. Strong candidates avoid these patterns by following a consistent template: clarify, verbalize approach, code with narration, test with examples.
Behavioral and design rounds have their own failure modes. Behavioral: stories that use "we" instead of "I" diluting individual signal, stories with no quantified outcome, defensiveness when probed about failure. Design: not asking clarifying questions, not stating requirements out loud, designing for a single server when the prompt clearly implies scale, ignoring operational concerns (deployment, monitoring, rollback). These show up in roughly half of Google Machine Learning Engineer interview retrospectives on LeakCode.
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