Carnegie Mellon University's Software Engineering Institute (SEI) is a premier research and development center that leads advancements in artificial intelligence (AI) and AI Engineering in defense and national security. As a Machine Learning Engineer at SEI, you'll be at the forefront of engineering solutions supporting Adversarial Machine Learning (AML). The AML Lab focuses on enhancing the security and robustness of AI systems through research and practical implementations, collaborating with government sponsors to develop mission-critical AI capabilities.

This guide, hosted by Interview Query, will help you navigate the interview process for this cutting-edge role, providing you with insights and tips to prepare effectively. Let’s get started!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining the Software Engineering Institute (SEI) at Carnegie Mellon University as a Machine Learning Engineer. Whether a recruiter contacted you or you've 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 is shortlisted, a recruiter from Carnegie Mellon's Talent Acquisition Team will contact you to verify key details like your experiences and skill level. Behavioral questions may also be a part of the screening process.

In some cases, the hiring manager may also be present during the screening round to answer your queries about the role and the organization itself. They may engage in surface-level technical and behavioral discussions.

The entire recruiter call should take about 30 minutes.

Technical Virtual Interview

Successfully navigating the recruiter round will lead to an invitation for the technical screening round. The technical screening for the Machine Learning Engineer role usually is conducted through virtual means, including video conferencing and screen sharing. Questions in this 1-hour long interview stage may revolve around SEI's AI systems, ETL pipelines, and machine learning frameworks like TensorFlow, PyTorch, and others.

You may also be assessed on your proficiency with topics such as computer vision, natural language processing, planning and scheduling, robot control, and other machine learning methods. Due to the high-security nature of the work, subjects like adversarial machine learning and algorithm defenses may be prominent in the discussions.

Onsite Interview Rounds

The onsite interview rounds are designed to thoroughly evaluate your technical and behavioral fit for the role. Over one or two days, multiple interviews will occur, focusing on different aspects of the job. These may include:

• Technical Deep-Dives: Hands-on coding exercises and discussions about algorithms, data structures, and machine learning principles.
• System Design Interviews: Evaluating your ability to design scalable, robust, and secure AI systems.
• Behavioral Interviews: Questions designed to understand how you interact with teams, manage projects, and handle challenges.

If you were assigned take-home exercises, a presentation round to showcase your work might be included.

Quick Tips For Carnegie Mellon University Machine Learning Engineer Interviews

Here are a few tips for acing your Software Engineering Institute (SEI) interview:

• Understand SEI’s Mission: SEI specializes in AI Engineering for Defense and National Security. Familiarize yourself with its unique mission and how machine learning fits into this context.
• Brush Up on Adversarial Machine Learning: Given the emphasis on improving the security and robustness of AI systems, ensure you have a good understanding of both offensive and defensive machine learning techniques.
• Practice Prototyping and Evaluation: Be ready to showcase your ability to develop, prototype, and evaluate machine learning solutions rapidly.

Typically, interviews at Software Engineering Institute | Carnegie Mellon University 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: Determine the time complexity.

• 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. Complexity of (O(n)) required.

• Develop 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. Write a function to search for a target value in the rotated array. If the value is in the array, 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 the number of job postings per day has 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 suspicious 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, and how would you reformat it? You have data on student test scores in two different layouts. Identify the drawbacks of the current organization, suggest formatting changes for better analysis, and describe common problems in "messy" datasets. Refer to the provided image of the datasets.

• 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 algorithm generates its forest. 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? You are comparing 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 building such a 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 Machine Learning Engineer at Software Engineering Institute | Carnegie Mellon University?

Q: What makes the SEI AI Division at Carnegie Mellon University unique?

The SEI AI Division conducts cutting-edge research in applied artificial intelligence, specifically focusing on the engineering challenges of designing and implementing AI technologies. We lead efforts to advance AI Engineering for Defense and National Security, by solving practical engineering problems, developing scalable AI capabilities, and preparing our customers for the challenges of adopting AI technologies.

Q: What are the primary responsibilities of a Machine Learning Engineer at SEI?

As a Machine Learning Engineer, you will design and build prototypes of AI systems, develop processes and tools for working with AI, and transition AI capabilities to government sponsors. Your role includes building machine learning models, conducting technical experimentation, and collaborating closely with researchers and developers to create secure and robust AI systems.

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

Applicants should have a bachelor's degree in computer science, machine learning, electrical engineering, or a related discipline, with extensive experience in machine learning. Preferred qualifications include previous experience in adversarial machine learning, excellent communication skills, and a proven track record of using established engineering practices to solve complex problems.

Q: What is the interview process like for this position?

The interview process typically involves several stages, including a recruiter call, technical interviews, and onsite interviews. It is designed to assess your technical expertise, problem-solving abilities, and fit within the company culture. You should be prepared to discuss your past projects, technical knowledge, and your approach to solving engineering challenges.

Q: How can I prepare for the interview?

To prepare for the interview, research Carnegie Mellon's SEI AI Division and its current projects. Utilize platforms like Interview Query to practice common interview questions, brush up on your technical skills, and review key concepts in machine learning and AI engineering. Be ready to discuss your experience and demonstrate your ability to apply machine learning techniques to real-world problems.

If you want more insights about the company, check out our main Software Engineering Institute Interview Guide, where we have covered many interview questions that could be asked. We’ve also created interview guides for other roles, such as machine learning engineer and data scientist, where you can learn more about Software Engineering Institute’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 Software Engineering Institute machine learning engineer 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!