Snorkel AI is on a mission to democratize AI with their definitive AI data development platform. Originating in the Stanford AI Lab in 2016, Snorkel has harnessed the transformative power of AI to help organizations build customized AI solutions swiftly. Now, they're inviting passionate professionals to join their team as an Applied Machine Learning Engineer.

As an Applied Machine Learning Engineer at Snorkel AI, you'll engage with new ML use cases, from data aggregation to model deployment, and work on state-of-the-art machine learning techniques. You will collaborate directly with customers to deliver impactful solutions and integrate feedback to improve the Snorkel Flow platform. If you're intellectually curious, energetic, and eager to make a global impact, Snorkel AI is the perfect place for you. Explore more about the interview process and prep yourself with Interview Query to excel in your application journey!

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 Machine Learning Engineer. 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 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 Machine Learning Engineer role is conducted through virtual means, including video conferences and screen sharing. Questions in this 1-hour long interview stage may revolve around machine learning systems, data aggregation and exploration, algorithm selection, and model deployment.

For this role, you might encounter questions assessing your proficiency in modern machine learning frameworks and technologies (e.g., PyTorch, Transformers, Scikit-learn, NumPy, Pandas) as well as your ability to build and maintain large-scale, production data pipelines.

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 skills, including programming and ML modeling 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 Machine Learning Engineer role at Snorkel AI.

Quick Tips For Snorkel AI Machine Learning Engineer Interviews

• Get Hands-On with Practical ML Techniques: Snorkel AI interviews assess your experience in applying machine learning techniques to real-world use cases. Make sure to review relevant ML frameworks and practice building end-to-end ML pipelines.
• Understand Snorkel’s Mission: Demonstrate your understanding of how Snorkel Flow empowers users to programmatically label and manage training data, and how this can democratize AI development.
• Be Prepared for Customer-Focused Scenarios: You may be asked to present findings and recommendations to stakeholders. Prepare to discuss your experience in technical program management and delivering business impact through AI/ML solutions.

Typically, interviews at Snorkel Ai vary by role and team, but commonly Machine Learning Engineer 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: What's the time complexity?

• Write 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. Complexity of (O(n)) required.

• Write a function precision_recall 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. You are given a target value to search. If the value is in the array, then return its index; otherwise, return -1. Bonus: Your algorithm's runtime complexity should be in the order of (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 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.

• Write a function to calculate sample variance from a list of integers. Create a function that takes a list of integers and returns the sample variance, rounded 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 suspicious about the A/B test results with 20 variants? 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 to find the median in a list where over 50% of elements are the same? Given a sorted list of integers where more than 50% of the list is the same 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 student test score data layouts? Analyze the drawbacks of the provided student test score data layouts (dataset 1 and dataset 2). Suggest formatting changes to make the data more useful for analysis and describe common problems seen in "messy" datasets.

• How would you evaluate whether using a decision tree algorithm is the correct model for predicting loan repayment? You are tasked with building a decision tree model to predict if a borrower will pay back a personal loan. How would you evaluate if a decision tree is the right choice, and how would you assess its performance before and after deployment?

• How does random forest generate the forest, and why use it over logistic regression? Explain the process by which a random forest generates its ensemble of trees. Additionally, discuss the advantages of using random forest over logistic regression.

• When would you use a bagging algorithm versus a boosting algorithm? Compare two machine learning algorithms. Describe scenarios where you would prefer a bagging algorithm over a boosting algorithm, and discuss 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 evaluate the model's accuracy and validity?

What is the average salary for a Machine Learning Engineer at Snorkel Ai?

Q: What is the mission of Snorkel AI?

Our mission is to democratize AI by building the definitive AI data development platform. We aim to empower diverse professionals to create custom AI models with their data faster than ever before.

Q: What does the role of an Applied Machine Learning Engineer at Snorkel AI involve?

As an Applied Machine Learning Engineer, you will research and implement state-of-the-art machine learning techniques to deliver valuable solutions to our customers. You'll tackle hands-on customer problems and help integrate new learnings into our core platform, Snorkel Flow. Responsibilities include end-to-end project delivery, presenting findings to stakeholders, and designing enterprise AI/ML solutions across multiple industries.

Q: What qualifications are preferred for the Applied Machine Learning Engineer position?

Preferred qualifications include: - Over 5 years of professional experience in machine learning or 2+ years with an advanced degree - At least 1 year of experience working directly with external customers on ML projects - Expertise in frameworks like PyTorch, Transformers, Scikit-learn, and a strong emphasis on thorough ML evaluation - Experience with large-scale, production data pipelines - Ability to thrive in a fast-paced environment and strong communication skills

Q: What is the salary range for this position?

For our Tier 1 locations (San Francisco, Seattle, Los Angeles, and New York), the salary range is $140,000.00 - $200,000.00. Additionally, all offers include equity compensation in the form of employee stock options.

Q: What benefits can Snorkelers expect?

Snorkel AI provides comprehensive medical, dental, and vision plans for employees and their families, plus a yearly wellness stipend. We offer a 401k program for future planning, and our parental leave program allows new parents up to 20 weeks of paid time off. Additional benefits include a workstation setup allowance and more, detailed on our Careers page.

At Snorkel AI, we're on a mission to democratize AI and redefine how organizations build AI applications. By joining us as an Applied Machine Learning Engineer, you'll be at the forefront of innovation, utilizing cutting-edge ML techniques to deliver impactful solutions across various industries. You'll work with a dynamic, mission-driven team, constantly prototyping new ways to add value and make a global impact. Ready to build the future of AI with us? Apply to become the newest Snorkeler!

If you want more insights about the company, check out our main Snorkel AI 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 Snorkel AI’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 Snorkel AI machine learning engineer interview question and challenge.

Good luck with your interview!