Interview Guide Aug 02
Aug 023 rounds
The role of a Reddit Machine Learning Engineer is to develop and deploy machine learning models that help to enhance user experience, improve content quality, and drive engagement on the platform. As a Machine Learning Engineer at Reddit, you will work alongside a team of Machine Learning Engineers and engineers to design, develop and deploy scalable ML models that can process the vast amount of data generated by the platform.
One of the main responsibilities of a Reddit Machine Learning Engineer is to develop and maintain recommendation systems that help users discover relevant content. This involves building models that can analyze user behavior, content quality, and other factors to make personalized recommendations to users. In addition, Machine Learning Engineers at Reddit work on developing models to detect and prevent spam, hate speech, and other forms of abusive behavior on the platform.
Reddit Machine Learning Engineers work in various teams, including Content Discovery, Trust and Safety, Ads and Revenue, Data Science Platform, and Growth. MLEs in the Content Discovery team create models to recommend relevant content to users. The Trust and Safety team works to prevent spam and abusive behavior by building models that detect and flag such content. The Ads and Revenue team builds models that optimize ad targeting and revenue generation. The Data Science Platform team develops APIs and tools for other teams to integrate machine learning into their products. Finally, the Growth team focuses on user acquisition and retention by developing models that optimize user engagement.
These are just a few of the many teams that Reddit Machine Learning Engineers may work in. Each team has its unique focus, but they all share a common goal of using machine learning to enhance user experience and drive engagement on the platform.
How to Apply for a Machine Learning Engineer Job at Reddit?
Take a look at Reddit's website and visit their careers page. You'll find plenty of opportunities available, and you can easily apply to roles directly on the site. However, we would highly recommend taking the referral route if you know someone in the company as it increases your chances significantly. Before you hit the apply button, make sure you read the job requirements thoroughly. Nothing's more frustrating than getting caught off guard during an interview. If you want to increase your chances even more, tailor your resume to align it with the qualifications and experiences listed in the job posting. It'll make you stand out from the rest. If you're not sure how to do that, Prepfully offers a resume review service, where actual recruiters will give you feedback on your resume.
As a part of the Reddit Machine Learning Engineer interview, you will need to go through multiple interview rounds:
1. Phone screening - The phone screening is a quick call to discuss your background and ML experience.
2. Technical Round - You will be asked to build a machine learning model based on data provided by the interviewer. This round is designed to evaluate your technical skills and ability to apply machine learning concepts to real-world problems.
3. The final round of the interview process at Reddit consists of multiple interview rounds.
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The phone screening is a quick call to discuss your background, ML experience, and culture fit. The interviewer will likely be a member of the recruiting team or a hiring manager. The purpose of the phone screening is to get a high-level overview of your ML background and experience, as well as assess their fit with the company culture.
- Why do you want to join Reddit?
- Why do you think you will be a good fit for the role?
- What responsibilities do you expect to have from your job at Reddit?
- What makes you the best candidate for this position?
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In the technical round, you will be asked to build a machine learning model based on data provided by the interviewer. This round is designed to evaluate your technical skills and ability to apply machine learning concepts to real-world problems. The interviewer will assess your proficiency in data cleaning and preprocessing, feature engineering, model selection and evaluation, and algorithm implementation and optimization. Your ability to communicate technical concepts clearly and concisely will also be evaluated. The interviewer may ask follow-up questions throughout the round to assess your thought process and problem-solving skills. You should come prepared with their preferred machine learning libraries and a working development environment. Additionally, the interviewer may ask questions related to any one section of machine learning from classical models to NLP, Deep Learning, or computer vision.
- How do you approach data cleaning and preprocessing?
- Can you give an example of a feature engineering technique you've used in a past project?
- How do you choose which model to use for a given problem?
- How do you evaluate the performance of your machine learning models?
- Can you walk me through how you would optimize the hyperparameters of a model?
- Which machine learning libraries are you most familiar with, and how have you used them in the past?
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The final primary round consists of several sub-rounds, each designed to assess different aspects of your technical and behavioral skills. You can expect to face:
- Behavioral Round: This round is designed to evaluate your behavioral fit for the role. The interviewer will ask questions related to your previous experience, work style, and problem-solving approach. The goal is to assess your communication skills, teamwork, and ability to work in a fast-paced, dynamic environment.
- ML System Design Round: In this round, you will be asked to design and implement a machine learning system based on a hypothetical scenario provided by the interviewer. The interviewer will ask questions about building a feature quickly and what tooling you would use, and then how they would adjust as they scale. The goal is to assess your ability to design and implement scalable, efficient, and effective machine learning systems.
- Algorithm Round: This round is designed to evaluate your proficiency in algorithm development and optimization. The interviewer may ask questions related to algorithm selection, optimization, and implementation. The goal is to assess your ability to develop and optimize machine learning algorithms for real-world problems.
- General-Purpose Programming: In this round, the interviewer will ask questions related to your general-purpose programming skills, such as data structures, algorithms, and object-oriented programming. The goal is to assess your proficiency in programming and their ability to develop software applications beyond machine learning.
- How do you approach problem-solving in your work? Can you walk us through your thought process?
- You've been given a hypothetical dataset and are tasked with building a machine learning system to predict a specific outcome. Can you walk us through your process for designing and implementing this system?
- How would you approach building a feature quickly in a machine learning system? What tooling would you use and how would you adjust as you scale?
- How do you ensure that your machine learning systems are scalable, efficient, and effective?
- Can you walk us through your thought process for selecting an algorithm to use for a particular problem?
- How do you optimize machine learning algorithms for real-world problems? Can you give an example?
- How do you approach debugging a software application?
Tips to Stand out in Reddit Machine Learning Engineer Interview
When you are preparing for a Reddit MLE interview - we’d recommend the following things to keep in mind:
- Learn as much as you can about Reddit, including its mission, values, and products. Look for recent news or events related to the company that may be relevant to the role you are applying for. Check out Reddit's values page for more information.
- Refresh your knowledge of fundamental machine learning concepts, such as data preprocessing, feature engineering, model selection, evaluation, and optimization.
- Review data structures, algorithms, and object-oriented programming concepts. Practice coding problems in your preferred programming language.
- Review your previous experiences and be prepared to discuss how you approached various problems or situations. Focus on examples that highlight your teamwork, communication skills, and ability to work in a dynamic environment.
- Explain your thought process and how you approach problem-solving. Focus on how you break down complex problems into manageable steps, analyze the data, and arrive at solutions.
Responsibilities of a Machine Learning Engineer at Reddit
The responsibilities of a Machine Learning Engineer at Reddit across roles can broadly be seen as-
- Propose, design, and implement high-performance ML platform solutions that significantly advance the deployment of models that serve millions of redditors.
- Mentor other team members in adopting a rigorous DevOps approach to maintain and/or improve ML platform components and services health and quality.
- Design and implement solutions that significantly advance the architecture of the ML Platform.
- Work with management on team goal setting, planning, and de-risk project execution. This would ensure that the team stays on track and delivers high-quality ML platform solutions on time.
- Participate in the full software development cycle: design, develop, QA, deploy, experiment, analyze and iterate.
Skills and Qualifications needed for Machine Learning Engineers at Reddit
Here are some skills and qualifications that will help you excel in your Machine Learning Engineering interviews at Reddit. One thing to note here is that the degree qualification is different for every role.
- It's beneficial to have at least 2+ years of experience in Data Science roles.
- Solid theoretical knowledge of Machine Learning and Statistical concepts, including Deep Learning, as well as performance tradeoffs.
- Reddit MLEs should have experience with at least one of the major ML frameworks, such as Tensorflow, Keras, PyTorch, and Sklearn. Having a good understanding of these frameworks and how to use them effectively is essential for building effective ML solutions.
- Experience working with data-intensive systems and writing production-quality software: Reddit MLEs should have experience working with data-intensive systems and be capable of writing production-quality software in languages such as Python or Golang.
- Experience with recommender and/or ranking systems is a plus, as it is a core part of the Content Discovery team's responsibilities.
- Knowledge of maintaining or developing applications using large-scale data stack, e.g, BigQuery, GraphQL, Kafka, Flink, Cassandra, Redis.
The average salary for a Machine Learning Engineer (MLE) at Reddit is around $150,000 per year, with a range of $110,000 to $200,000 per year depending on experience and other factors. It's important to note that salaries can vary based on a number of factors such as location, specific job responsibilities, and level of experience.
The interview process for a Machine Learning Engineer role at Reddit typically includes 3 primary rounds - a phone screening, technical round, and the final interview rounds. The phone screening is a quick call to discuss your background and ML experience. In the technical round, you will be asked to build a machine learning model based on data provided by the interviewer. This round is designed to evaluate your technical skills and ability to apply machine learning concepts to real-world problems. The final primary round of the interview process at Reddit consists of multiple interview rounds including behavioral, ML system design, and general purpose programming rounds.