Snorkel AI is a pioneering company born from the Stanford AI Lab in 2016, focused on revolutionizing how AI applications are built by leveraging programmatically labeled and managed training data. At Snorkel AI, we empower organizations worldwide to create custom AI solutions more efficiently, ensuring high performance and production-readiness.

As an Applied Research Scientist, you will transform cutting-edge research into practical user experiences. The role encompasses a variety of tasks such as prompting models like GPT-4 effectively, building domain-specific foundation models, and innovating ways to efficiently label large-scale documents. Your work will involve state-of-the-art model iteration, prototyping novel workflows, and contributing to groundbreaking research in AI. The position offers a hybrid schedule and emphasizes collaboration and empirical excellence.

Join us in redefining AI development by applying through Interview Query for an opportunity to become the newest Snorkeler!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining Snorkel AI as a Research Scientist. Whether you were contacted by a Snorkel AI recruiter or have taken the initiative yourself, carefully review the job description and tailor your CV according to the prerequisites.

Tailoring your CV may include identifying specific keywords that the hiring manager might use to filter resumes and crafting a targeted cover letter. Furthermore, don’t forget to highlight relevant skills and mention your work experiences.

Recruiter/Hiring Manager Call Screening

If your CV happens to be among the shortlisted few, a recruiter from the Snorkel AI Talent Acquisition Team will make contact and verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the Snorkel AI Research Scientist hiring manager stays present during the screening round to answer your queries about the role and the company itself. They may also indulge in surface-level technical and behavioral discussions.

The whole recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will present you with an invitation for the technical screening round. Technical screening for the Snorkel AI Research Scientist role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around Snorkel AI’s data systems, machine learning frameworks, and specific technical questions related to AI, NLP, and generative AI.

In the case of Research Scientist roles, take-home assignments regarding model iteration, analysis, and prototyping end-to-end workflows may be incorporated. Apart from these, your proficiency against hypothesis testing, coding hygiene, and model training on large datasets may also be assessed during the round.

Depending on the seniority of the position, case studies and similar real-scenario problems may also be assigned.

Onsite Interview Rounds

Followed by a second recruiter call outlining the next stage, you’ll be invited to attend the onsite interview loop. Multiple interview rounds, varying with the role, will be conducted during your day at the Snorkel AI office. Your technical prowess, including programming and model prototyping capabilities, will be evaluated against the finalized candidates throughout these interviews.

If you were assigned take-home exercises, a presentation round may also await you during the onsite interview for the Research Scientist role at Snorkel AI.

Quick Tips for Snorkel AI Research Scientist Interviews

• Stay Updated on AI Trends: Snorkel AI places a high emphasis on cutting-edge research and trends in AI, NLP, LLM, and generative AI. Make sure you are updated on recent developments and can discuss them effectively.
• Demonstrate Practical Application: Be prepared to discuss how you have translated research into practical applications. Give examples of how your work has resulted in state-of-the-art model iterations and real-world impacts.
• Showcase Coding and Prototyping Skills: Snorkel AI values robust coding hygiene and the ability to prototype novel algorithms. Make sure to brush up on standard machine learning frameworks and be ready to showcase your skills during the technical rounds.

Typically, interviews at Snorkel Ai vary by role and team, but commonly Research Scientist interviews follow a fairly standardized process across these question topics.

• 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?

What is the average salary for a Research Scientist at Snorkel Ai?

Q: What does an Applied Research Scientist at Snorkel AI do? As an Applied Research Scientist at Snorkel AI, you will innovate and translate cutting-edge research into user experiences. Your responsibilities include developing state-of-the-art approaches for data-centric model iteration, prototyping end-to-end workflows, and collaborating with design partners to validate your work on real-world use cases.

Q: What qualifications are Snorkel AI looking for in an Applied Research Scientist? Snorkel AI prefers candidates with broad expertise in AI, NLP, LLM, and generative AI trends. Experience in prototyping, experimenting with large-scale datasets, and using machine learning frameworks like TensorFlow and PyTorch are highly valued. Strong technical communication skills and the ability to work in a fast-paced environment are essential.

Q: Can I apply if I don't meet all the preferred qualifications listed in the job posting? Yes, Snorkel AI encourages applications from candidates who believe they satisfy many of the preferred qualifications, even if they do not satisfy all. They value diverse skill sets and unique career paths.

Q: What is the work environment like at Snorkel AI? Snorkel AI operates on a hybrid schedule with remote work flexibility and designated "No Meeting" days. Their team is supported with comprehensive benefits, including medical, dental, and vision plans, a wellness stipend, a 401k program, and paid parental leave.

Q: What is the salary range for the Applied Research Scientist position? The salary range for Tier 1 locations such as San Francisco, Seattle, Los Angeles, and New York is $182,000.00 - $235,000.00, with additional equity compensation in the form of employee stock options.

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!