Draper is an independent, nonprofit research and development organization based in Cambridge, MA, with over 2,000 employees dedicated to creating innovative solutions for national challenges. From military defense to biomedical engineering, Draper’s multidisciplinary teams deliver critical, life-saving innovations.

As a Machine Learning Engineer at Draper, you'll design, implement, and operationalize machine learning solutions on large, complex datasets. Your work will range from extracting insights from cyber, language, and vision data to performing research in adversarial machine learning, algorithmic fairness, and model interpretability. You will collaborate with cross-disciplinary teams using diverse technologies such as embedded systems, microelectronics, and network operations.

Explore Draper’s impactful work and prepare for your interview with Interview Query. This guide will help you navigate the interview process, familiarize you with commonly asked questions, and provide valuable tips to help you succeed. 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 Draper as a Machine Learning Engineer. Whether you were contacted by a Draper 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 Draper 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 Draper Machine Learning Engineer hiring manager will be 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 Draper Machine Learning Engineer role is usually conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour-long interview stage may revolve around Draper's data systems, security vulnerabilities, adversarial machine learning, algorithmic fairness, and model interpretability.

In some cases, take-home assignments will be provided concerning specific aspects of machine learning and cyber tool development. Apart from these, your proficiency against hypothesis testing, probability distributions, and fundamentals of machine learning 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 Draper office in Cambridge, MA. Your technical prowess, including programming and machine learning 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 Draper.

Quick Tips for Draper Machine Learning Engineer Interviews

You should plan to brush up on any technical skills and try as many practice interview questions and mock interviews as possible. A few tips for acing your Draper interview include:

• Understand National Challenges: Draper focuses on critical national challenges in various fields like military defense and space exploration. Study how machine learning can contribute to these areas.
• Familiarize with Cybersecurity: Given the position’s focus on cybersecurity and machine learning, ensure you are well-versed with adversarial machine learning, embedded systems, and network operations.
• Be a Collaborative Player: Draper values team players who can work in a multidisciplinary environment. Prepare to demonstrate your ability to collaborate and communicate effectively across various technical domains.

Typically, interviews at Draper vary by role and team, but commonly Machine Learning Engineer interviews follow a fairly standardized process across these question topics.

• Identify all duplicate values in a list of integers. Given a list of integers, identify all the duplicate values in the list. Assume that the list can contain both positive and negative numbers, and the order of the list does not matter. A number is considered a duplicate if it appears more than once in the list. Return a list of the duplicate numbers.

• Select the five most expensive projects by budget to employee count ratio. We want to select the five most expensive projects by budget to employee count ratio. Account for duplicate rows in the employee_projects table and write a query to select the top five projects.

• Create a subquery or common table expression to find the top 3 ads by popularity. Create a subquery or common table expression named top_ads containing the top 3 ads (by popularity) and return the number of rows resulting from different join operations with the ads table.

• Find employees who joined before their manager. You're given two tables: employees and managers. Find the names of all employees who joined before their manager.

• Write a function to rotate a matrix by 90 degrees clockwise. Given an array filled with random values, write a function rotate_matrix to rotate the array by 90 degrees in the clockwise direction.

• How do we select the best 10,000 customers for a pre-launch of a new show? You are working as a data scientist at Amazon Prime Video, and they want to test the launch of a new show on 10,000 customers first. How would you select the best 10,000 customers for this pre-launch?

• What is the process for pre-launching a TV show on Amazon Prime to measure performance? Describe the steps involved in pre-launching a TV show on Amazon Prime to measure its performance.

• How would you evaluate the results of an A/B test on free shipping to determine success? You work at an eCommerce startup and ran an A/B test on the checkout product page to see if surfacing free shipping increases conversions. The control group had no specification of free shipping, while the experiment group did. How would you evaluate the results and determine if the test was successful?

• How would you conduct an experiment to test displaying ETA as a range instead of a direct estimate? You work at Uber, and a PM suggests displaying ETA as a range (e.g., 3-7 minutes) instead of a direct estimate (e.g., 5 minutes). How would you conduct this experiment and determine if the results are significant?

• How would you decide whether Google should build a game feature for Google Home? You are tasked with pitching a new feature for Google Home, and a co-worker suggests building a game feature. How would you decide whether Google should build it?

• How would you measure the effectiveness of giving extra pay to delivery drivers during peak hours? You work at a food delivery company and need to measure the effectiveness of giving extra pay to delivery drivers during peak hours to meet consumer demand. How would you measure this?

• What is the difference between Logistic and Linear Regression? When would you use one instead of the other? Explain the key differences between Logistic and Linear Regression. Describe scenarios where each method is more appropriate to use in practice.

• How would you explain what a p-value is to someone who is not technical? Explain the concept of a p-value in simple terms to someone without a technical background. Use analogies or everyday examples to make it understandable.

• What is the difference between Logistic and Linear Regression? When would you use one instead of the other in practice? Describe the key differences between Logistic and Linear Regression. Provide practical scenarios where each type of regression would be appropriately applied.

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

Q: What is Draper and what do they do?

A: Draper is an independent, nonprofit research and development company based in Cambridge, MA. With over 2,000 employees, Draper tackles national challenges in areas such as military defense, space exploration, and biomedical engineering by delivering innovative and usable solutions.

Q: What will my responsibilities be as a Machine Learning Engineer at Draper?

A: As a Machine Learning Engineer, you will design, implement, and operationalize machine learning solutions for complex tasks on large data sets. This includes extracting insights from various data sources, performing research in areas such as adversarial machine learning, algorithmic fairness, and model interpretability. You’ll also assess system security, develop software to exploit vulnerabilities, and work collaboratively with multidisciplinary teams.

Q: What skills and qualifications are Draper looking for in a Machine Learning Engineer?

A: Draper seeks candidates with a bachelor's degree in computer science, computer engineering, or related fields, and 5-10 years of experience in cybersecurity or related areas. Ideal candidates should possess a curiosity-driven approach to problem-solving, collaborate effectively with multidisciplinary teams, and be able to work in a fast-paced environment. Additionally, some positions may require obtaining and maintaining a government security clearance.

Q: How does Draper support work-life balance for its employees?

A: Draper values work-life balance and offers various programs to support it. These include workplace flexibility, employee clubs, health and finance workshops, off-site social events, and discounts to local museums and cultural activities.

Q: How can I prepare for an interview at Draper?

A: To prepare for an interview at Draper, it's crucial to research the company and understand its mission and values. Practice common interview questions and review your technical skills using resources like Interview Query. Be ready to discuss your past experiences, technical projects, and problem-solving skills.

If you're ready to be part of a mission-driven team at the forefront of innovation in national defense, space exploration, and biomedical engineering, the Machine Learning Engineer position at Draper is your perfect fit. To gain deeper insights into Draper and its interview process, check out our main Draper Interview Guide. At Interview Query, we equip you with the tools needed to master your interview and secure your spot at Draper. Don’t miss this chance to be part of a collaborative environment where your contributions can truly make a difference. Explore our company interview guides for comprehensive preparation tips. Best of luck with your interview!