Snorkel AI is on a mission to democratize AI by building the definitive data development platform, empowering organizations to create custom AI solutions efficiently.

As a Research Scientist at Snorkel AI, you will be at the forefront of translating cutting-edge research into practical applications, particularly focusing on foundational multimodal problems and data development techniques. You will be responsible for establishing state-of-the-art approaches for data-centric model iteration and analysis, prototyping innovative workflows, and collaborating with design partners to validate your work in real-world scenarios. A successful candidate will have a strong background in applied research, particularly in computer vision, and will be adept at handling large-scale datasets, including medical imaging. Experience with machine learning frameworks, cloud infrastructure, and a passion for tackling complex, unsolved problems will be essential. This role aligns closely with Snorkel AI's commitment to innovation, collaboration, and real-world impact, making it a vital part of the team.

This guide will prepare you to navigate the unique expectations and challenges of interviewing at Snorkel AI, ensuring you are equipped to showcase your relevant skills and experiences effectively.

The interview process for the Research Scientist role at Snorkel AI is designed to assess both technical expertise and cultural fit within the innovative environment of the company. Here’s what you can expect:

1. Initial Screening

The first step in the interview process is a 30-minute phone call with a recruiter. This conversation will focus on your background, experiences, and motivations for applying to Snorkel AI. The recruiter will also provide insights into the company culture and the specifics of the Research Scientist role, ensuring that you have a clear understanding of what to expect moving forward.

2. Technical Assessment

Following the initial screening, candidates typically undergo a technical assessment, which may be conducted via video conferencing. This assessment is designed to evaluate your problem-solving skills and technical knowledge in areas relevant to the role, such as machine learning, computer vision, and data-centric model iteration. You may be asked to solve coding problems, discuss your previous research, and demonstrate your understanding of algorithms and data handling techniques.

3. Collaborative Interview

The next stage involves a collaborative interview with team members, including other research scientists and possibly design partners. This round focuses on your ability to work in a team setting and your approach to real-world problem-solving. You may be presented with case studies or hypothetical scenarios that require you to demonstrate how you would apply your research skills to develop innovative solutions.

4. Onsite Interview

The onsite interview consists of multiple rounds, typically lasting around 45 minutes each. During these sessions, you will engage with various team members, including senior researchers and leadership. The discussions will cover a range of topics, including your past research contributions, technical skills, and how you can contribute to Snorkel AI's mission. Expect to delve into specific projects you've worked on, as well as your experience with large-scale datasets and machine learning frameworks.

5. Final Interview

The final interview is often with a senior leader or the hiring manager. This conversation will focus on your long-term career goals, alignment with Snorkel AI's vision, and how you can contribute to the company's growth. It’s also an opportunity for you to ask questions about the team dynamics, company culture, and future projects.

As you prepare for these interviews, it’s essential to reflect on your experiences and how they align with the responsibilities of the Research Scientist role. Next, let’s explore the types of questions you might encounter during the interview process.

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

Emphasize Your Research Impact

At Snorkel AI, the ability to translate cutting-edge research into practical applications is crucial. Be prepared to discuss specific projects where your research led to measurable outcomes. Highlight how your work has contributed to advancements in multimodal problems or data-centric model iteration. Use concrete examples to illustrate your impact and how you collaborated with design partners to validate your findings in real-world scenarios.

Showcase Your Technical Proficiency

Given the technical nature of the Research Scientist role, it’s essential to demonstrate your expertise in relevant frameworks and tools. Be ready to discuss your experience with machine learning libraries such as TensorFlow, PyTorch, and data manipulation tools like Pandas and NumPy. If you have experience with large-scale datasets, especially in computer vision or medical imaging, make sure to highlight this. Prepare to explain your approach to prototyping and testing models, as well as any innovative techniques you’ve developed.

Prepare for Problem-Solving Scenarios

The role involves tackling complex problems that often lack off-the-shelf solutions. During the interview, you may be presented with hypothetical scenarios or challenges. Approach these questions methodically: clarify the problem, outline your thought process, and discuss potential solutions. Emphasize your ability to innovate on-the-fly and how you would leverage your research background to address these challenges.

Understand the Company Culture

Snorkel AI values diversity, collaboration, and personal growth. Familiarize yourself with their mission to democratize AI and how they support underrepresented communities in tech. During the interview, express your alignment with these values and how you can contribute to fostering an inclusive environment. Share experiences that demonstrate your commitment to teamwork and collaboration, especially in fast-paced settings.

Communicate Clearly and Effectively

Strong technical communication skills are essential for this role. Practice articulating complex concepts in a clear and concise manner. Be prepared to explain your research and technical decisions to both technical and non-technical audiences. This will not only showcase your expertise but also your ability to collaborate effectively with cross-functional teams.

Be Ready for a Hybrid Work Environment

Snorkel AI operates on a hybrid work model, with specific "No Meeting" days. Discuss your experience working in remote or hybrid settings and how you manage your time and productivity in such environments. Highlight your adaptability and any strategies you use to maintain effective communication and collaboration with team members.

Ask Insightful Questions

Prepare thoughtful questions that demonstrate your interest in the role and the company. Inquire about ongoing projects, the team’s approach to research, or how they measure the impact of their work. This not only shows your enthusiasm but also helps you gauge if Snorkel AI is the right fit for you.

By following these tips, you’ll be well-prepared to showcase your qualifications and fit for the Research Scientist role at Snorkel AI. Good luck!

Snorkel AI Research Scientist Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Snorkel AI Research Scientist interview. The interview will focus on your ability to innovate in applied research, particularly in the areas of computer vision and multimodal data. Be prepared to discuss your experience with data-centric model iteration, prototyping workflows, and collaborating on real-world applications.

Machine Learning and Data Development

1. Can you describe a project where you developed a data-centric model? What challenges did you face and how did you overcome them?

This question assesses your practical experience with data-centric models and your problem-solving skills.

How to Answer

Discuss a specific project, detailing the model's purpose, the data used, and the challenges encountered. Highlight your innovative solutions and the impact of your work.

Example

“In my recent project, I developed a model for medical image classification using a large dataset of CT scans. One challenge was the imbalance in the dataset, which I addressed by implementing data augmentation techniques. This not only improved the model's accuracy but also made it more robust in real-world applications.”

2. How do you approach prototyping end-to-end workflows for machine learning models?

This question evaluates your workflow design and prototyping skills.

How to Answer

Explain your process for prototyping, including the tools and methodologies you use. Emphasize your focus on iterative testing and validation.

Example

“I typically start by defining the problem and gathering requirements. I then create a prototype using tools like TensorFlow or PyTorch, focusing on modular design for easy adjustments. After initial testing, I collaborate with design partners to refine the workflow based on real-world feedback.”

3. Describe your experience with large-scale datasets. How do you ensure data quality and integrity?

This question probes your experience with data management and quality assurance.

How to Answer

Discuss your strategies for handling large datasets, including data cleaning, validation, and monitoring processes.

Example

“In my previous role, I worked with a large dataset of medical images. I implemented a rigorous data validation process, including automated checks for missing values and outliers. This ensured high data quality, which was crucial for the model's performance.”

4. What techniques do you use for model evaluation and iteration?

This question assesses your understanding of model performance metrics and iterative improvement.

How to Answer

Outline the evaluation metrics you prioritize and how you use them to iterate on your models.

Example

“I focus on metrics like precision, recall, and F1 score, depending on the application. After evaluating the model, I analyze the errors to identify patterns and areas for improvement, which informs my next iteration.”

5. Can you explain a time when you had to innovate on-the-fly during a project?

This question looks for your ability to adapt and innovate under pressure.

How to Answer

Share a specific instance where you had to think creatively to solve an unexpected problem.

Example

“During a project, we encountered a significant data loss issue just before a deadline. I quickly devised a strategy to use synthetic data generation techniques to fill the gaps, allowing us to meet our timeline without compromising the model's integrity.”

Computer Vision and Multimodal Data

1. What is your experience with 3D computer vision, and how have you applied it in your research?

This question evaluates your expertise in a specialized area of computer vision.

How to Answer

Discuss specific projects or research where you utilized 3D computer vision techniques.

Example

“I worked on a project involving 3D reconstruction from 2D images for augmented reality applications. I implemented algorithms for depth estimation and object recognition, which significantly enhanced the user experience in the final product.”

2. How do you handle multimodal data in your research?

This question assesses your ability to integrate and analyze data from different modalities.

How to Answer

Explain your approach to working with multimodal data, including any frameworks or techniques you use.

Example

“I often use techniques like cross-modal retrieval and joint embedding spaces to handle multimodal data. In a recent project, I combined image and text data to improve the accuracy of a visual question-answering system, which resulted in a 20% increase in performance.”

3. Describe your experience with medical imaging datasets. What unique challenges do they present?

This question probes your familiarity with a specific application of computer vision.

How to Answer

Discuss the challenges of working with medical imaging data, such as privacy concerns and data variability.

Example

“Working with medical imaging datasets, I faced challenges like data privacy and the need for high accuracy. I ensured compliance with regulations while implementing robust validation techniques to account for variability in imaging conditions, which was crucial for the model's reliability.”

4. Can you explain the importance of image-text alignment in your work?

This question evaluates your understanding of a key concept in multimodal research.

How to Answer

Discuss how image-text alignment impacts model performance and user experience.

Example

“Image-text alignment is critical for tasks like image captioning and visual question answering. I focus on creating models that effectively learn the relationships between images and text, which enhances the model's ability to generate accurate and contextually relevant outputs.”

5. What advancements in computer vision do you find most exciting, and how do you see them impacting your work?

This question assesses your awareness of current trends and innovations in the field.

How to Answer

Share your thoughts on recent advancements and their potential applications in your research.

Example

“I’m particularly excited about the advancements in transformer-based models for vision tasks. Their ability to capture long-range dependencies in data can significantly improve performance in complex tasks like object detection and segmentation, which I plan to explore in my future projects.”

• 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 of your solution.

• Create a function missing_number to find the missing number in an array. 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. The complexity should be (O(n)).

• Develop a function precision_recall to calculate precision and recall metrics. 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. The algorithm's runtime complexity should be (O(\log n)).

• Would you think there was anything fishy about the results of an A/B test with 20 variants? Your manager ran an A/B test with 20 different variants and found one significant result. Would you suspect any issues with these results?

• How would you set up an A/B test to optimize button color and position for higher click-through rates? A team wants to A/B test changes in a sign-up funnel, such as changing a button from red to blue and/or moving it from the top to the bottom of the page. How would you design this test?

• What would you do if friend requests on Facebook are down 10%? A product manager at Facebook reports a 10% decrease in friend requests. What steps would you take to address this issue?

• Why might the number of job applicants be decreasing while job postings remain constant? You observe that job postings per day have remained stable, but the number of applicants has been steadily decreasing. What could be causing this trend?

• What are the drawbacks of the given student test score datasets, and how would you reformat them for better analysis? You have data on student test scores in two different layouts. What are the drawbacks of these formats, and what changes would you make to improve their usefulness for analysis? Additionally, describe common problems in "messy" datasets.

• Is this a fair coin? 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? Write a function that outputs the sample variance given a list of integers. Round the result to 2 decimal places. Example input: test_list = [6, 7, 3, 9, 10, 15]. Example output: get_variance(test_list) -> 13.89.

• Is there anything fishy about the A/B test results? Your manager ran an A/B test with 20 different variants and found one significant result. Evaluate if there is anything suspicious about these results.

• How do you find the median in (O(1)) time and space? Given a list of sorted integers where more than 50% of the list is the same repeating integer, write a function to return the median value in (O(1)) computational time and space. Example input: li = [1,2,2]. Example output: median(li) -> 2.

• What are the drawbacks of the given data organization? You have data on student test scores in two different layouts. Identify the drawbacks of these layouts, suggest formatting changes for better analysis, and describe common problems in "messy" datasets. Refer to the provided image for dataset examples.

• 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 decision 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?

If you're enthusiastic about innovating in AI and making a major impact, Snorkel AI is the place to be. As an Applied Research Scientist, you’ll tackle groundbreaking questions like effectively prompting GPT-4, building domain-specific foundation models, and optimizing weakly supervised models at scale. Want more insights about the company? Check out our main Snorkel AI Interview Guide, where we cover many potential interview questions. We also have interview guides for other positions, such as software engineer and data analyst, detailing Snorkel AI's interview process for diverse roles. At Interview Query, we equip you with the knowledge, confidence, and strategic guidance to master your interview with Snorkel AI. Get prepared and feel free to reach out with any questions. Good luck with your interview!