Thursday, June 20, 2024

Twitter Machine Learning Engineer Interview

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What Are The Types Of Data Scientists At Twitter

Amazon Machine Learning Engineer Interview: K-Means Clustering

Twitter has a data science and analytics department with research scientists and data scientists working across a wide range of teams. Whether its in the scaled enforcement heuristics team, consumer product team, or the home and explore team, data scientists in these teams use the latest and most advanced analytics tools and machine learning models to provide business impact recommendations and improve products. Depending on the teams, the job role may include the following:

  • Create sophisticated statistical models that learn and scale to streaming data.
  • Create and interpret sophisticated SQL inquiries for standard and impromptu data mining functions.
  • Interpret and influence crowdsourcing and human calculation procedures for data labelling.
  • Partner closely with product and engineering teams to create and assess data-driven product roadmaps

Interview Questions For Google Machine Learning Engineer

If you want to become a Google Machine Learning engineer, youve to take into careful consideration your interview process and rounds where a bunch of different questions shall be asked. Below mentioned are a few sample questions that are most asked during the interview process of a Google Machine Learning Engineer:

The Role Of A Twitter Data Scientist

Twitter is an American microblogging and social networking service on which users post and interact with limited-word messages known as “tweets”. With an active user base of 353 million in 2021, Twitter is one of the world’s largest technology companies.

Data Scientist salary at Twitter:

  • Entry-level salary: USD 176,000.
  • Senior positions: USD 386,000.
  • Median salary: USD 225,000 with base component being USD 160,000, stock component being USD 50,000 and bonus being USD 15,000.


The exact role of a data scientist at Twitter depends on the team one is working for. Here are some of the data science teams that are part of Twitter:

Data Science teams at Twitter:

  • Consumer Product
  • Home and Explore

Here are some of the common responsibilities Twitter data scientists across the board are expected to fulfil.

Skills/Qualifications preferred

  • 2+ years experience in data science and quantitative analysis
  • Demonstrable first principle problem-solving skills
  • Strong programming skills and experience using common analysis tools
  • Strong bias to action, creative problem-solving mindset, and proactive communication
  • An advanced degree in a quantitative domain such as Computer Science, Machine Learning, Statistics, Operations Research, or similar. Masters and PhD is a plus but not required
  • Proficiency with ML and data analytics technologies such as Spark, Airflow, TensorFlow, etc.
  • Prior work experience with building India focused technology products is a plus.

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Needed Skills As A Google Machine Learning Engineer

As a Google Machine Learning Engineer, there are some crucial skills youll need to acquire or brush up on before you can start working and living your dream at Google in the best position of 21st century.

Statistics: Tools and tables are critical in creating models from data in machine learning. Statistics and its subdivisions, such as analysis of variance and hypothesis testing, are essential for algorithm development. We can see how crucial statistics are for machine learning since machine learning algorithms are founded on statistical models. That is how statistics play an important part in algorithm development. As a result, understanding statistical tools is critical if you want to further your career in machine learning.

Probability: Probability aids in forecasting future outcomes because the bulk of machine learning algorithms operate under uncertain settings and must make accurate judgments. Probabilistic mathematical equations such as derivative approaches, Bayes Nets, and Markov choices would aid machine learning in forecasting the future.

Data Modeling: The key objective of machine learning is to interpret unstructured data models, which necessitates data modeling science. Data modeling allows for the identification of underlying data structures, the discovery of trends, and the filling of gaps where data is missing.

Having a detailed understanding of data modeling ideas will aid in the development of efficient algorithms.

Twitter Data Science Interview Questions

How Data Found Frank La Vigne

Twitter is known for its news and debates and holds the title of the SMS of the world. Created in 2006 by Jack Dorsey, Noah Glass, Evan Williams, and Biz Stone, it has grown to have more than 321 million active users per month as well as 1.6 billion search inquiries each day.

Aside from being one of the biggest tech companies, Twitter also has one of the worlds largest real-time datasets. To manage such large amounts of data, Twitter has a dedicated data science and analytics teams that employ advanced analytics and machine learning tools to improve their products and features toward delivering more relevant content on their feeds.

Read Also: How To Interview For An Hr Position

What Do I Get

If you select the monthly or yearly plans then you get access to over 120 questions and answers spanning almost all relevant topics in ML from Linear Regression to Generative Adversarial Networks. In addition, you will get access to 7 ML system design questions and 6 ML coding questions which are a core part of every ML Engineer interview.

Twitter Machine Learning Engineer Interview Jobs

Hello all, We are looking for people who are electrical, mechanical, biology, chemical, science, mathematics , operation management, science engineer or have done specialization in these subjects in Tukey. They should be native to Turkey, should speak fluent Turkish and should be able to speak, write and understand English as well. Its a 6 month contract where you will be paid every month and can extend depending on the demand. Its a 9 hour full time job with 1 hour break in between. Payment will be made in USD.

we are looking for a person who can follow our – instagram- opensea -telegram groups

We are looking for someone to help us design, build, and maintain a business facebook page, instagram, and tictok. We are looking for someone to copy an initial design from a referance page, as well as post relevant content on a daily basis from premade reels, and content. All new members or subscribers we would like to follow up with them with premade content and scripts to guide them to one of our live events. We are also looking to use a list we will provide to reach out to new potential clients using look alike groups and paid marketing.The right person will be able to create initial pages, post daily, follow up daily with clients, and create ad campaigns to drive our campaign using our customer lists.

Also Check: How To Prepare For Facebook Sql Interview

Twitter Machine Learning Engineer Interview

Twitter machine Learning Engineer job interview case study, sample questions: coding, machine learning system design, behavioral question.

An instamentor mentee Joey recently went through a virtual onsite interview at Twitter and received a job offer as a machine learning engineer.

Here is a summary of what Uber’s virtual onsite interview process looked like.

Google Machine Learning Engineer Salary

Interview With My Brother Who Sold His Startup For $60 Million | Machine Learning Engineer

Average Annual Salary | Estimated Take Home Salary

80,00,000 | 4,39,675 4,52,580/month

Average Google Machine Learning Engineer pay in India is 80 Lakhs per year for professionals with experience between 6 years and 7 years. Google Machine Learning Engineers salary range from 30 to 100 lakhs per year. Salary estimations are based on four salaries collected from Google workers.

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Twitter Data Science Interview

Last Earnings, Twitter Inc. soared the most since its market debut in 2013 after it posted the first revenue growth in four quarters, driven by improvements to its app and added video content that are persuading advertisers to boost spending on the social network Bloomberg

Twitter has one of the biggest data sets in the world. It is much different from Facebook from the aspect that Twitter is real time. Twitter data sets are awesome troves of information and provide great insights. Working on some Twitter data set and providing valuable insights can be a good portfolio project to showcase. One can get twitter data here.

The interview process usually consists of phone interview with the hiring manager. On site interviews consists of meeting with Engineers/Data Scientists. The questions are usually algorithmic in nature including some machine learning questions, math/application based questions and one system design question around working on a distributed system to deliver high scale machine learning.

Important Reading

  • Twitter Data Case Studies: Use Cases to inform business decisions
  • AI/Data Science Related Questions

    Reflecting on the Questions

    Twitter Machine Learning Infrastructure Engineer Interview Questions


    Anonymous Interview Candidate in New York, NY

    I applied online. I interviewed at Twitter


    onsite consisted of one machine learning system design round, the problem was a very well known problem found on grokking the system design.The algorithmic interviews were leetcode medium and a leetcode hard. I did not fully finish the leetcode hard but the interviewer was satisfied.I also had a behavioral round with a hiring manager. I was rejected due to the behavioral due to poor prep.

    Interview Questions

    • The algorithmic interviews were leetcode medium and a leetcode hard. I did not fully finish the leetcode hard but the interviewer was satisfied.I also had a behavioral round with a hiring manager.

    Also Check: What Are The Most Common Questions For A Job Interview

    Requirements As A Google Machine Learning Engineer

    Minimum requirements:

    A bachelors degree in computer science or a closely related technical field, or equivalent work experience is required.

    Work experience of at least 5 years in a relevant field.

    Experience with distributed software systems design and implementation.

    Artificial Intelligence , Machine Learning models, ML infrastructure, Natural Language Processing , or Deep Learning research or industry experience is required.

    Qualifications that are preferred:

    A masters or doctoral degree in Computer Science, Artificial Intelligence, Machine Learning, or a closely related technical field is required.

    2 years of relevant work experience in the development of machine learning software and architectures .

    One or more of the following areas of expertise: Full Stack Development , Scalable Enterprise Platforms and Applications, Application Security and Incident Management, Machine Learning, Information Retrieval, or Natural Language Processing are all examples of server backend distributed and parallel systems.

    Building, deploying, and improving Machine Learning models and algorithms in real-world products is required.

    To find out more about Google Machine Learning job descriptions and requirements, please take a look at the below job listings from Google:

    Interview Experience For New Grad Software Engineer Role At Twitter India

    International Women in Engineering Day

    Hello World,

    Im Atibhi Agrawal, Ill be graduating with an Integrated Masters in Engineering from IIIT-Bangalore in July 2021. After that, I will be joining Twitter India as a software engineer. I will be based in Bangalore.

    In this blog post, I am going to tell you about the application process, interview process and the resources that I used for preparation.

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    What Is The Data Science Role

    The data scientist job position at Twitter is split into both data and research scientist roles. Twitters data science roles are tailored-specific to the teams they are assigned to. Each Twitters data science role is also different from one another. Data scientist job roles at Twitter depend heavily on the teams theyre assigned to in specific features or services, and the role may span from analytics-based roles to model design and building heavy machine learning systems.

    Interview Process And Timeline

    Whats the Facebook machine learning engineer interview process and timeline? It normally follows the below steps and takes four to eight weeks to complete:

  • Application and referrals
  • Coding interview
  • Onsite interviews
  • Next, well dig into each of these steps in more detail. If youre interviewing with multiple companies, take a look at our guides to the Google ML engineer interview and the Amazon ML engineer interview.

    Step one is getting a Facebook interview in the first place. In this guide were focusing primarily on the interviews, so well keep this portion brief. You can apply to Facebook directly or a recruiter may reach out to you via LinkedIn . In either case, it helps to have a quality resume that is tailored to machine learning positions, and to Facebook more specifically.

    It can also be helpful to get an employee referral to the Facebook recruiting team internally. This may not be possible, but if you do have a connection to someone who works at Facebook, then this can help you get your foot in the door for an interview.

    In most cases, youll start your interview process with Facebook by talking to an HR recruiter. They are looking to confirm that youve got a chance of getting the job at all, so be prepared to explain your background and why youre a good fit at Facebook. You should expect typical behavioral and resume questions like, tell me about yourself, why Facebook?, or tell me about your current project.

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    Mlops: What It Is Why It Matters And How To Implement It

    13 mins read | Prince Canuma | Posted January 14, 2021

    According to techjury, every person created at least 1.7 MB of data per second in 2020. For data scientists like you and me, that is like early Christmas because there are so many theories/ideas to explore, experiment with, and many discoveries to be made and models to be developed.

    But if we want to be serious and actually have those models touch real-life business problems and real people, we have to deal with the essentials like:

    • acquiring & cleaning large amounts of data
    • setting up tracking and versioning for experiments and model training runs
    • setting up the deployment and monitoring pipelines for the models that do get to production.

    And we need to find a way to scale our ML operations to the needs of the business and/or users of our ML models.

    There were similar issues in the past when we needed to scale conventional software systems so that more people can use them. DevOps solution was a set of practices for developing, testing, deploying, and operating large-scale software systems. With DevOps, development cycles became shorter, deployment velocity increased, and system releases became auditable and dependable.

    That brings us to MLOps. It was born at the intersection of DevOps, Data Engineering, and Machine Learning, and its a similar concept to DevOps, but the execution is different. ML systems are experimental in nature and have more components that are significantly more complex to build and operate.

    Machine Learning Engineer Vs Data Scientist

    What Does A Machine Learning Engineer At Amazon Do?

    I mentioned that people use these terms interchangeably. Its a mistake to do so because there is a difference between the two posts. In fact, the main work of Data scientists is more about building a good model where Machine Learning engineers tend to focus on the deployment of the model and how to ship it in the production environment.

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    When Should I Get The : 1 Mock Interviews

    You should undertake these interviews when you are almost ready to interview with companies. Each 1:1 interview gives you 2 hours of personal time with the question creators. We will schedule two 1 hour sessions, one core ML questions and the other on an ML System Design question. You will receive detailed feedback on your performance and how you can improve.

    A Guide For Machine Learning Technical Interviews

    This repo aims to serve as a guide to prepare for Machine Learning engineer interviews for roles at big tech companies . It has compiled based on authors personal experience and notes from his own interview preparation in 2020, when he received offers from Facebook , Google , Amazon , Apple , and Roku.


    • At the time I’m putting these notes together, machine learning interviews at different companies do not follow a unique structure unlike software engineering interviews. However, I found some of the components very similar to each other, although under different naming.

    • The guide here is mostly focused on Machine Learning Engineer roles at big companies. Although relevant roles such as “Data Science” or “ML research scientist” have different structures in interviews, some of the modules reviewed here can be still useful. For more understanding about different technical roles within ML umbrella you can refer to

    • As a supplementary resource, you can also refer to my Production Level Deep Learning repo for further insights on how to design deep learning systems for production.

    The following components are the most commonly used interview modules for technical ML roles at different companies. We will go through them one by one and share how one can prepare:

    • Feedback and contribution are very welcome

    Also Check: How To Crack Amazon Data Engineer Interview

    Discuss The Core Features

    So firstly divide the whole system into several core components and talk about some core features. If some other features your interviewer wants to include he/she will mention there. For now, we are going to consider the following features on Twitter

    • The user should be able to tweet in just a few seconds.
    • The user should be able to see Tweet Timeline
    • Timeline: This can be divided into three parts
    • User timeline: User sees his/her own tweets and tweets user retweet. Tweets that users see when they visit their profile.
    • Home Timeline: This will display the tweets from people users follow.
    • Search timeline: When users search some keywords or #tags and they see the tweets related to that particular keywords.
    • The user should be able to follow another user.
    • Users should be able to tweet millions of followers within a few seconds

    Resources Used For Preparation

    Machine Learning Is Just Statistics Meme

    The resources that I have mentioned are not Twitter specific and can be used for any interviews. I had been consistent with Leetcode for the past year. Note that I did not cover all of the theory material in two weeks. I had been preparing since September 2020. So it took me approximately three and half months to cover everything.

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