Google Machine Learning Engineer Interview Questions

Jane D. - "My approach to dealing with difficult stakeholders has always been: Engage - Directly engage with the stakeholder, meet or chat Listen - Listen to what they have to say, patiently. Understand - Understand their POV, even if it is impossible at some times Ask - Ask clarifying questions. Why? When? What? Engage again - Keep them in the loop until there is closure For example, we were in the final stages of a very important, strategic project for our organization. I was leading th"See full answer

Nicola R. - "You shouldn't hire me if you're looking for someone to simply write code in large volumes without considering the bigger picture. I'm someone who thrives on solving root problems, building, cohesive systems, and ensuring stakeholder alignment. If the priority is speed over thoughtful analysis, I might not be the best fit. However, if you're looking for someone who can drive meaningful and scalable solutions, collaborate effectively, and contribute to long-term success, then I believe I'd bring s"See full answer

Varun G. - "Constrain the requirements (The final product should be somewhat like Neptune AI and also enable for A/B testing) Do some back of the envelope calculations Start explaning your system with appropriate architecture diagrams Optimize for scale Answer any questions Wrap-up"See full answer

叶 路. - "from collections import deque def updateword(words, startword, end_word): if end_word not in words: return None # Early exit if end_word is not in the dictionary queue = deque([(start_word, 0)]) # (word, steps) visited = set([start_word]) # Keep track of visited words while queue: word, steps = queue.popleft() if word == end_word: return steps # Found the target word, return steps for i in range(len(word)): "See full answer

Ushita S. - "A clarifying question: Is this question asking about when I met a tight deadline in a project or how did I manage a project that had a tight deadline? The answer uploaded to this question is good, I would also add 'creating a critical path from overall project schedule and then making sure that none of the deliverables in the critical path are sacrificed in order to meet the tight deadline' as an action taken."See full answer

Yeshwanth D. - "Was this for an entry level engineer role?"See full answer

Aabid S. - "I work at a startup that makes software for Law Enforcement and the FBI. Our product analyzes calls being made by prison inmates and "listens" for predictors of violence and criminal behavior. Our clients are some of the top state prisons in the country. Recently one of the largest states in the country decided to evaluate our product for their prison system. I demo'd the product to the officers and they seemed to like everything. During the presentation they asked us if the product was ADA com"See full answer

Bipin R. - "As you know, this is the most important question for any interview. Here is a structure I like to follow, Start with 'I'm currently a SDE/PM/TPM etc with XYZ company.... ' Mention how you got into PM/TPM/SDE field (explaining your journey) Mention 1 or 2 accomplishments Mention what you do outside work (blogging, volunteer etc) Share why are you looking for a new role Ask the interviewer if they have any questions or will like to dive deep into any of your experience"See full answer

Ashley C. - "I follow a variation of the RICE framework when prioritizing how I ship product features. I start by looking at: Reach: Because the customer segmentation across our product portfolio is so similar, I tend to hold a lot of weight on product features that will maximize our customer reach with a minimal LOE. Impact: After establishing which customer segments will benefit from the product feature, I determine the urgency and estimated impact on each customer segment based on customer i"See full answer

Million D. - "Machine learning software engineer interviews at Google are really challenging. The questions are difficult, specific to Google, and they cover a wide range of topics."See full answer

Anchal V. - "supervised learning: model is trained on the labeled data unsupervised learning: no labels provided - model learns by finding patterns , structure and groupings in the data. Semi-supervised learning: use small set of labels to guide learning for the larger pool of unlabeled data. reinforcement learning: leans by interacting with students the environment, receives reward and penalties based on actions self supervised: no labelled data . The model makes its own practice problems by"See full answer

Rajesh V. - "As a PM i received a feedback from my program manager on my style of verbal communication. It is about me speaking faster when i wanted to get away with a topic that i wasn't confident (may be not backed up with data, or still in process of getting detailed insight of a problem etc.). Whereas when I'm confident I tend to speak slowly or more assertively that made people to follow easily. I welcomed that feedback so from then on when I'm not confident in a topic I became more assertive to let pe"See full answer

Wayne W. - "def hasgoodsubarray(nums, k): if not nums: return False prefix = 0 table = set([0]) for i in range(len(nums)): prefix += nums[i] if prefix % k in table: return True table.add(prefix % k) return False `"See full answer

Anonymous Possum - "#inplace reversal without inbuilt functions def reverseString(s): chars = list(s) l, r = 0, len(s)-1 while l < r: chars[l],chars[r] = chars[r],chars[l] l += 1 r -= 1 reversed = "".join(chars) return reversed "See full answer

Praful B. - "The company culture is very supportive and collaborative. Googlers are encouraged to be creative and innovative, and there is a lot of freedom to explore new ideas. The work is challenging and rewarding. Googlers have the opportunity to work on cutting-edge projects that have a real impact on the world. The company is committed to diversity and inclusion. Google is a great place to work for people from all backgrounds and with all different perspectives. I am confident that I would b"See full answer

Kofi N. - "const mergeIntervals = (intervals) => { const compare = (a, b) => { if(a[0] b[0]) return 1 else if(a[0] === b[0]) { return a[1] - b[1] } } let current = [] const result = [] const sorted = intervals.sort(compare) for(let i = 0; i = b[0]) current[1] = b[1] els"See full answer

fuzzyicecream14 - "App I Don’t Like: Electrify America What Electrify America Is Supposed to Do Electrify America is an app designed to help electric vehicle (EV) owners locate, access, and pay for fast-charging stations across the U.S. The app provides real-time station availability, allows users to initiate and monitor charging sessions, and offers membership plans for discounted rates. Ideally, it should enable a seamless charging experience, especially for long-distance travelers relying on i"See full answer

Louie Z. - "DNNs can learn hierarchical features, with each layer learning progressively more abstract features, and generalizes better. SNNs are better for simplier problems involving smaller datasets and if low latency is required."See full answer

Srini K. - "performance issues and sudden spikes on input requests by scaling techniques and optimization."See full answer