Machine Learning Engineer Interview Questions

Anonymous Aardvark - "Recently, I had to make a decision that I could automate a part of a process now and help the operations team reducing 9 hours of manual work to 60 minutes for one client. The second option was to fully automate the end to end process that would take 4 weeks of development, but fully automate the process and that could be rolled out across the board. The process was to change the member's paperless preference to paper when a sent email gets bounced 3 times in a row and inform her with a paper"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

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

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

Jaehyuk C. - "Q: What ad system do we have (to clarify the limitation of the ads systems and its scope)? -> need context of the advertisement. What signals of ads do we have? pictures, texts, comments, video, etc. What is daily active users on the system? (scalability) Do we need taking actions after detecting it? (further process is needed?) what device do we have ad system? PC, mobile, etc. FR: detect the weapon signals (classification) alert after weapon is detected identify the us"See full answer

Ajay P. - "Designing an evaluation framework for ads ranking is crucial for optimizing the effectiveness and relevance of ads displayed to users. Here's a comprehensive framework that you can use: Define Objectives and Key Performance Indicators (KPIs):** \\Click-Through Rate (CTR):\\ The ratio of clicks to impressions, indicating the effectiveness of an ad in attracting user attention. \\Conversion Rate:\\ The ratio of conversions (e.g., sign-ups, purchases) to clicks, measuring how well"See full answer

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

Mayank M. - "There are 2 main methods Intrinsic Evaluation i) Preplexity ii) BLEU Extrinsic Evaluation i) Response consistency/ Correctness / Factual score/ Security However, this question requires a follow-up question and clarification about where we are going to use the LLM models."See full answer

Adam L. - "Discussed: Requirements of the system: latency language modality (assume keyboard typing) availability of data (assume cold start) success metric (accuracy of next word predicted?, or minimize false positives? -> accuracy to start) Data collection and processing: design ethical user experiments to collect typed out data design a simple tokenization strategy (word level encoding, character level encoding, byte-pair encodings, and discuss tradeoffs) collect data, and split"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

Alex N. - "Since question asks about pipeline. I assume the question is about metrics across many dimensions not just prediction Model performance. For the ML Model: I can use accuracy, precision, recall, F1 if it is classification model. In case it is regression model RMSE is good metric for many problems. Data: ML system needs good quality data. The system has to track missing data rate. Distribution of features, if there is no drift from original feature distributions during the training. Pipeline h"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

Malay K. - "For any project based questions, it is important to structure your response clearly, showcasing your thought process, technical skills, problem-solving abilities, and how your work added value. Besides the STAR method, you can also use this kind of framework: 1. Start by selecting a relevant project (related to the role) Give the project background and what specific problem it solved. 2. Align the project's objective and your role Be specific about your role: were you the le"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

Alex N. - "The adjusting context window size in LLM change trade off between reasoning capability, accuracy, computation cost. It influence how attention mechanist allocate resources across the input. Longer context window let it you input greater number of words and have more context to generate proper next token. However llms have lost in the middle issue. They remember the beginning of text and end of text but lost information located in the middle of long input. Another problem is Attention Dilution."See full answer

Anonymous Partridge - "We will have to use a second more powerful LLM Model to validate the answers. LLM as a judge"See full answer