Snap Inc. is a technology company dedicated to enhancing communication and self-expression through innovative products leveraging camera technology.

The Research Scientist role at Snap Inc. is integral to the Creative Vision Research Team, which focuses on transforming technology into creative tools that empower users to become creators. This position encompasses leading a multi-year research agenda and executing large-scale multimodal generative projects that will directly impact Snap's products. Candidates must exhibit strong technical knowledge in statistics, machine learning, and deep learning literature, along with demonstrated experience in defining and leading complex research projects. Proficiency in programming languages such as Python, C, or C++ is essential, as is a proven ability to mentor and lead junior researchers and interns.

A successful Research Scientist at Snap will have a PhD in a relevant technical field, a track record of publications in top-tier research conferences, and a passion for using cutting-edge technology to enhance user experiences. This role aligns with Snap's commitment to innovation and collaboration, as well as its diverse and inclusive workplace culture.

This guide will help you prepare for your interview by providing insights into the expectations and skills required for the role, setting you up for success in showcasing your qualifications and fit for Snap’s innovative environment.

The interview process for a Research Scientist at Snap Inc. is designed to assess both technical expertise and cultural fit within the innovative environment of the company. The process typically consists of several key stages:

1. Initial Screening

The first step is an initial screening, which usually takes place over a video call. During this conversation, a recruiter will discuss your background, the role, and what it’s like to work at Snap. This is an opportunity for you to express your interest in the position and to highlight your relevant experiences, particularly those related to research and technology.

2. Technical Interview

Following the initial screening, candidates typically undergo a technical interview. This interview is often conducted via video conferencing and focuses on your technical skills and knowledge in areas such as machine learning, computer vision, and deep learning. You may be asked to solve problems on the spot or discuss your previous research projects in detail, showcasing your ability to lead and execute complex research agendas.

3. Presentation of Research

A unique aspect of the interview process at Snap Inc. is the requirement to present your research. Candidates are usually asked to prepare a presentation that outlines their past work, methodologies, and findings. This presentation is typically followed by a Q&A session where interviewers will probe deeper into your research, assessing both your technical understanding and your ability to communicate complex ideas effectively.

4. Onsite Interviews

The final stage often involves onsite interviews, which may include multiple rounds with different team members. These interviews will cover a mix of technical and behavioral questions, allowing the interviewers to evaluate your problem-solving skills, teamwork, and how well you align with Snap's values. Expect to engage in discussions about your approach to research, collaboration with engineering teams, and how you can contribute to Snap's innovative projects.

Throughout the process, candidates are encouraged to be themselves and to engage in a casual yet professional manner, reflecting the company’s culture of openness and creativity.

Now, let’s delve into the specific interview questions that candidates have encountered during this process.

Here are some tips to help you excel in your interview.

Embrace a Casual Yet Professional Attitude

Snap Inc. values a relaxed and friendly interview atmosphere, as indicated by previous candidates' experiences. While you don’t need to wear a suit, it’s essential to maintain a professional demeanor. Dress smartly but comfortably, and be prepared to engage in a conversational style. This approach will help you feel at ease and allow your personality to shine through.

Showcase Your Research Passion

As a Research Scientist, your ability to lead and execute a multi-year research agenda is crucial. Be prepared to discuss your past research projects in detail, emphasizing your passion for innovation and creativity. Highlight how your work aligns with Snap's mission to enhance communication through technology. Sharing your enthusiasm for the field will resonate well with the interviewers.

Prepare for Technical Discussions

Given the technical nature of the role, you should be ready to discuss your expertise in machine learning, computer vision, and deep learning. Brush up on relevant algorithms, frameworks, and your past experiences with large-scale generative projects. Be prepared to explain complex concepts in a way that is accessible, as you may need to communicate your ideas to team members from diverse backgrounds.

Highlight Collaboration and Leadership Skills

Snap Inc. emphasizes teamwork and collaboration. Be ready to discuss your experiences leading teams, mentoring interns, or collaborating with engineers. Provide examples of how you’ve successfully partnered with others to deliver impactful research or technology. This will demonstrate your ability to thrive in a team-oriented environment.

Familiarize Yourself with Snap's Products and Culture

Understanding Snap's core products—Snapchat, Lens Studio, and Spectacles—will give you an edge. Familiarize yourself with how your research could enhance these products or contribute to new innovations. Additionally, embrace Snap's "default together" policy, which emphasizes in-person collaboration. Be prepared to discuss how you can contribute to a dynamic team culture.

Prepare for a Presentation

Candidates have reported that presenting their research is a part of the interview process. Prepare a concise and engaging presentation that showcases your work, focusing on its relevance to Snap's goals. Practice delivering your presentation to ensure clarity and confidence, and be ready to answer questions that may arise.

Be Authentic and Open

Snap Inc. values diversity and inclusion, and they are looking for candidates who can bring unique perspectives to the team. Be authentic in your responses and share your personal experiences and insights. This openness will help you connect with the interviewers and demonstrate that you align with Snap's values.

By following these tips, you will be well-prepared to make a strong impression during your interview for the Research Scientist role at Snap Inc. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Research Scientist interview at Snap Inc. Candidates should focus on demonstrating their technical expertise, problem-solving abilities, and experience in leading research projects. Be prepared to discuss your past work, particularly in relation to machine learning, computer vision, and generative models.

Machine Learning

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial for this role.

How to Answer

Clearly define both terms and provide examples of algorithms used in each category. Highlight the scenarios where each type is applicable.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as classification tasks using algorithms like decision trees or support vector machines. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, such as clustering with k-means or hierarchical clustering.”

2. Describe a machine learning project you led. What challenges did you face?

This question assesses your leadership and problem-solving skills in a research context.

How to Answer

Discuss the project scope, your role, the challenges encountered, and how you overcame them. Emphasize your contributions and the impact of the project.

Example

“I led a project on developing a generative model for image synthesis. One major challenge was ensuring the model could generalize well to unseen data. I implemented data augmentation techniques and fine-tuned hyperparameters, which improved the model's performance significantly, leading to a publication in a top-tier conference.”

3. How do you evaluate the performance of a machine learning model?

This question tests your understanding of model evaluation metrics.

How to Answer

Discuss various metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, and explain when to use each.

Example

“I evaluate model performance using accuracy for balanced datasets, but for imbalanced datasets, I prefer precision and recall. For instance, in a fraud detection model, high recall is crucial to minimize false negatives, so I focus on optimizing the F1 score to balance precision and recall.”

4. What is overfitting, and how can it be prevented?

This question gauges your understanding of model training and generalization.

How to Answer

Define overfitting and discuss techniques to prevent it, such as regularization, cross-validation, and using simpler models.

Example

“Overfitting occurs when a model learns noise in the training data rather than the underlying pattern, leading to poor performance on unseen data. To prevent this, I use techniques like L1/L2 regularization, dropout in neural networks, and cross-validation to ensure the model generalizes well.”

5. Can you explain the concept of transfer learning?

This question assesses your knowledge of advanced machine learning techniques.

How to Answer

Define transfer learning and provide examples of its application, particularly in computer vision.

Example

“Transfer learning involves taking a pre-trained model on a large dataset and fine-tuning it on a smaller, task-specific dataset. For instance, using a model trained on ImageNet for a specific image classification task can significantly reduce training time and improve performance.”

Computer Vision

1. What are some common techniques used in image processing?

This question tests your foundational knowledge in computer vision.

How to Answer

Discuss techniques such as filtering, edge detection, and image segmentation, and their applications.

Example

“Common techniques include Gaussian filtering for noise reduction, Canny edge detection for identifying edges, and segmentation methods like k-means clustering to partition images into meaningful regions for analysis.”

2. How do convolutional neural networks (CNNs) work?

This question assesses your understanding of deep learning in the context of computer vision.

How to Answer

Explain the architecture of CNNs, including convolutional layers, pooling layers, and fully connected layers.

Example

“CNNs consist of convolutional layers that apply filters to extract features, followed by pooling layers that reduce dimensionality. This architecture allows CNNs to learn spatial hierarchies of features, making them highly effective for image classification tasks.”

3. Describe a time you used computer vision techniques in a project.

This question evaluates your practical experience in applying computer vision.

How to Answer

Share a specific project, the techniques used, and the outcomes achieved.

Example

“In a project aimed at real-time object detection, I implemented YOLO (You Only Look Once) for its speed and accuracy. The model was trained on a custom dataset, and we achieved a 90% accuracy rate, which significantly improved the user experience in our application.”

4. What is the role of data augmentation in training computer vision models?

This question tests your understanding of improving model robustness.

How to Answer

Explain how data augmentation helps in creating a more diverse training dataset.

Example

“Data augmentation involves applying transformations like rotation, scaling, and flipping to the training images, which helps the model generalize better by exposing it to various scenarios and reducing overfitting.”

5. How do you handle large-scale image datasets?

This question assesses your experience with data management and processing.

How to Answer

Discuss techniques for efficient data handling, such as using cloud storage, data pipelines, and distributed computing.

Example

“I utilize cloud storage solutions for scalability and implement data pipelines using tools like Apache Spark to process large datasets efficiently. This allows for parallel processing and reduces the time required for training models on extensive image collections.”

Research and Publication

1. Can you discuss a research paper you published and its impact?

This question evaluates your research experience and ability to communicate findings.

How to Answer

Summarize the paper's objectives, methods, results, and significance in the field.

Example

“I published a paper on a novel generative adversarial network (GAN) architecture that improved image synthesis quality. The work was well-received, leading to collaborations with industry partners and influencing subsequent research in the area of generative models.”

2. How do you stay updated with the latest research in your field?

This question assesses your commitment to continuous learning.

How to Answer

Discuss your strategies for keeping abreast of new developments, such as reading journals, attending conferences, and participating in online forums.

Example

“I regularly read journals like IEEE Transactions on Pattern Analysis and Machine Intelligence and attend conferences such as NeurIPS and CVPR. Additionally, I engage with online communities and follow key researchers on platforms like Twitter to stay informed about the latest advancements.”

3. Describe your experience mentoring junior researchers or interns.

This question evaluates your leadership and teaching abilities.

How to Answer

Share specific examples of how you guided others in their research projects.

Example

“I mentored several interns during my PhD, helping them design experiments and analyze data. I organized weekly meetings to discuss progress and challenges, which not only supported their growth but also fostered a collaborative research environment.”

4. What strategies do you use to define and execute a research agenda?

This question assesses your planning and organizational skills.

How to Answer

Discuss how you identify research questions, set goals, and measure progress.

Example

“I start by conducting a literature review to identify gaps in existing research. I then outline a multi-year agenda with specific milestones and regularly assess progress through peer feedback and self-evaluation to ensure alignment with my research goals.”

5. How do you approach collaboration with engineering teams?

This question evaluates your ability to work cross-functionally.

How to Answer

Discuss your experience in translating research findings into practical applications and working with engineers.

Example

“I prioritize clear communication and collaboration by involving engineering teams early in the research process. This ensures that our findings are feasible for implementation, and I often participate in joint meetings to align our objectives and timelines.”

• Write a SQL query to select the 2nd highest salary in the engineering department. Write a SQL query to select the 2nd highest salary in the engineering department. If more than one person shares the highest salary, the query should select the next highest salary.

• Write a function to merge two sorted lists into one sorted list. Given two sorted lists, write a function to merge them into one sorted list. Bonus: Determine the time complexity.

• Write a function to find the missing number in an array of integers spanning 0 to n. You have an array of integers, nums of length n spanning 0 to n with one missing. Write a function missing_number that returns the missing number in the array. Complexity of (O(n)) required.

• Write a function to calculate precision and recall metrics from a 2-D matrix. Given a 2-D matrix P of predicted values and actual values, write a function precision_recall to calculate precision and recall metrics. Return the ordered pair (precision, recall).

• Write a function to search for a target value in a rotated sorted array. Suppose an array sorted in ascending order is rotated at some pivot unknown to you beforehand. Write a function to search for a target value in the array and return its index, or -1 if not found. Bonus: Achieve (O(\log n)) runtime complexity.

• How would you measure success for Facebook Stories? Determine the key performance indicators (KPIs) for Facebook Stories. Consider metrics such as user engagement, daily active users, time spent on Stories, and user retention. How would you measure and analyze these metrics to define success?

• How would you decide whether or not to create a product, like a job board, for Facebook? Imagine Facebook is considering creating a job board. What factors and data would you analyze to decide whether to proceed with this product? How would you evaluate its potential impact and success?

• How would you design an A/B test to evaluate the effectiveness of a Facebook job board? Imagine Facebook is creating a job board. How would you set up an A/B test to evaluate its effectiveness? Specify the metrics you would track and the criteria for determining success.

• How do we measure the launch of Robinhood’s fractional shares program? As a data scientist at Robinhood, how would you measure the success of the fractional shares program launch? Identify the key metrics and methods for evaluating the program's impact on user behavior and overall platform performance.

• How would you evaluate the suitability and performance of a decision tree model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will repay a personal loan. How would you evaluate whether a decision tree is the correct model for this problem? If you proceed with the decision tree, how would you evaluate its performance before and after deployment?

• How does random forest generate the forest and why use it over logistic regression? Explain how a random forest generates its forest of trees. Additionally, discuss why you might choose random forest over other algorithms like logistic regression.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. In which scenarios would you use a bagging algorithm versus a boosting algorithm? Provide examples of the tradeoffs between the two.

• How would you justify using a neural network model and explain its predictions to non-technical stakeholders? Your manager asks you to build a neural network model to solve a business problem. How would you justify the complexity of this model and explain its predictions to non-technical stakeholders?

• What metrics would you use to track the accuracy and validity of a spam classifier? You are tasked with building a spam classifier for emails and have completed a V1 of the model. What metrics would you use to track the accuracy and validity of the model?

• Is this a fair coin if it comes up tails 8 times out of 10 flips? You flip a coin 10 times, and it comes up tails 8 times and heads twice. Determine if the coin is fair based on this outcome.

• How do you write a function to calculate sample variance for a list of integers? Write a function that outputs the sample variance given a list of integers. Round the result to 2 decimal places.

Example: python test_list = [6, 7, 3, 9, 10, 15] Output: python get_variance(test_list) -> 13.89

• Is there anything suspicious about finding a significant variant in an A/B test with 20 variants? Your manager runs an A/B test with 20 different variants and finds one significant result. Evaluate if there is anything suspicious about these results.

• How do you find the median of a list where more than 50% of the elements are the same? Given a list of sorted integers where more than 50% of the list is comprised of the same repeating integer, write a function to return the median value in (O(1)) computational time and space.

Example: python li = [1,2,2] Output: python median(li) -> 2

• What are the drawbacks of the given student test score data layouts, and how would you reformat them? Assume you have data on student test scores in two different layouts. Identify the drawbacks of these layouts, suggest formatting changes to make the data more useful for analysis, and describe common problems seen in "messy" datasets.

Dataset 1 and 2:

What is the average salary for a Research Scientist at Snap Inc.?

Q: What is the interview process at Snap Inc. like for the Research Scientist position?

The interview process at Snap Inc. typically involves multiple stages, including an initial recruiter call, technical interviews focusing on ML system design and coding, and onsite interviews. Interviewers are encouraging and often provide hints if you're stuck. Be prepared for an overall positive and engaging experience.

Q: What kind of projects will I work on as a Research Scientist at Snap Inc.?

As a Research Scientist, you'll lead and support research projects in user modeling and personalization. You'll build scalable research prototypes, evaluate them in large-scale scenarios, publish findings at top conferences, and collaborate with engineering teams to implement your solutions for millions of users.

Q: What skills and qualifications are necessary to be successful in this position?

Key qualifications include a PhD in a related technical field (e.g., computer science, statistics, machine learning), a strong track record of publications, experience with ML libraries like PyTorch and TensorFlow, and experience in large-scale machine learning. Preferred skills include transforming cutting-edge research into product improvements and familiarity with distributed data processing on cloud platforms.

Q: What is the company culture like at Snap Inc.?

Snap Inc. values dynamic collaboration and believes in a "default together" approach, expecting team members to work in the office 4+ days per week. The company champions diversity and fosters an environment where various backgrounds and voices come together to innovate products that improve lives and communication.

Q: How can I best prepare for an interview at Snap Inc.?

To prepare for an interview at Snap Inc., research the company, its products, and the role. Brush up on your technical skills and review common interview questions on platforms like Interview Query. Practice system design and coding challenges to ensure you're ready for technical assessments.

Conclusion

Joining Snap Inc. as a Research Scientist on the User Modeling and Personalization Research Team presents a remarkable opportunity to pioneer cutting-edge ML solutions that redefine personalized user experiences. Snap Inc. is a vibrant technology company passionate about enhancing how people live and communicate through innovative products like Snapchat, Lens Studio, and Spectacles.

If you want more insights about the company, check out our main Snap Inc Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as software engineer and data analyst, where you can learn more about Snap’s interview process for different positions.

At Interview Query, we empower you to unlock your interview prowess with a comprehensive toolkit, equipping you with the knowledge, confidence, and strategic guidance to conquer every Snap Inc. Research Scientist interview question and challenge.

You can check out all our company interview guides for better preparation, and if you have any questions, don’t hesitate to reach out to us.

Good luck with your interview!