Machine Learning Engineer System Design Interview Questions

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

Alok S. - "Functional requirements: user can send an input and wait for the result Group up to 100 individual requests in to single GPU The system should should send results back to the user who requested it when done Non functional requirements: Minimize the waiting between two batches of execution/ reduce idle time error message if a batch faiils Scale to support multiple GPUs Core Entities: Request Batch Result API Design: POST /predict -> {requestid: "", response: ""} req"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

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

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

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

BriskD - "I watched a couple of videos like this, one of them by Exponent staff (I think). It was disappointing that the architecture diagram and the walkthrough was a general layered architecture that you can apply to any backend system. I was wondering if there are videos that can be considered a reference material to watch, learn and improve on the tech (sys design) skills, and not so much about the soft skills."See full answer

Kartikeya N. - "C : Okay. So I would want to start with knowing what is the product for which we have to build a recommendation system. I : This is a photo sharing product. C : Okay. So is this something on the lines of Instagram? I : Yes C : Okay. And are we a new product co or we have some current product built already? I : You can assume yourself. C : Okay. Is there any demography or country we are targeting? I : No, this is a global product C : Okay. So, the biggest goal of any product recommendation system"See full answer

Jacky Y. - "Problem scope: Can this system detect Bot in real-time online or offline? Both. Online traffic: 1M DAU. Latency: 2s. Offline frequency: daily Offline data: 2B activity logs. Data: How do we know a Bot player (Label)? Human label. Imbalance data: reweight, resample. Develop a Bot simulator to generate more data offline for training. Given lower weight to simulator data than human label. Features: Signals from different models online. Log all the features for offline. Propose new features: dail"See full answer

Sai vuppalapati M. - "At a high level, the core challenge here revolves around building an effective recommendation algorithm for news. News is an inherently diverse category, spanning various topics and catering to a wide array of user types and personas, such as adults, business professionals, general readers, or specific cohorts with unique interests. Consequently, developing a single, one-size-fits-all recommendation algorithm is not feasible. To enhance the personalization of the news recommendation algorithm,"See full answer

Michael F. - "Prompt: We work for an online shopping website. Our team wants to consider offering discounts (e.g. 10% off your next purchase) to customers to incentivize them to make purchases. How would you design a system that decides how to offer these incentives? Answer Goals: Increase customer engagement while controlling costs. Specifically, we want the increase in revenue per customer per week of customers that receive the discount to be greater than the cost of the discount. Metrics: Revenue per cu"See full answer

Clayton P. - "The interviewer hinted that a two-tower recommender system might be a suitable approach, using user history to embed users and pages separately and train on view or interaction data. Instead, I proposed a different approach that I felt was more aligned with how knowledge is structured in Confluence: I designed a system using a graph database to model the relationships between Confluence pages. Each page is a node, and edges represent content-based references. For example, when one article"See full answer

Scott S. - " Functional Requirements Content Ingestion\: Ingest news articles from various sources (websites, social media, etc.). Handle different types of content (text, images, videos). Content Analysis\: Extract and preprocess text from articles. Analyze the content for potential indicators of fake news. Model Training and Prediction\: Use machine learning models to classify content as fake or real. Continuously improve models with new data and f"See full answer

Anonymous Hare - "Sharing the approach for functional requirements we tool to solve this question. Functional Requirements This is only for the Registered users What is a "For You" page ? Home page where you get suggestions based on people you follow. Interactions like/share/comments (done by user) Interests (shared by the user during registration or onboarding) sports choices/ region choices/ Video sharing platform. So how many videos should we s"See full answer

Deepak K. - "FN Given text need to figure out is it following guidelines. Should notify the user in case of not following guidelines. Reason for failure should have misleading/spam/adult filters. NFN High availability High Scalability Low latency of processing Estimations 1M requests/min text - 10kb => 9.5GB/min => 14TB/day API fetchmoderationscore(text) score will be between 0 to 1 more than 0.8 => not following guidelines fetchmoderationscore(text, filter)"See full answer