GoPro, an innovative leader in action cameras, is renowned for its cutting-edge technology and immersive content experiences. The company has revolutionized the way people capture and share their adventures, making it a staple for adventurers, athletes, and vloggers alike.

Joining GoPro as a Machine Learning Engineer is an exhilarating journey, demanding a blend of technical prowess and creativity. This role entails harnessing advanced machine learning algorithms to enhance video stabilization, improve image quality, and develop smart editing tools. You'll be working closely with a dynamic team, pushing the boundaries of what's possible in video technology.

In this guide, Interview Query will walk you through the interview process, commonly asked questions, and tips tailored for aspiring Machine Learning Engineers at GoPro. Let's delve into this exciting opportunity and help you navigate your path to becoming a part of GoPro's innovative team!

Submitting Your Application

The first step is to submit a compelling application that reflects your technical skills and interest in joining GoPro as a Machine Learning Engineer. Whether you were contacted by a GoPro 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 GoPro 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 GoPro Machine Learning Engineer 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 GoPro Machine Learning Engineer role usually is conducted through virtual means, including video conference and screen sharing. Questions in this 1-hour long interview stage may revolve around GoPro’s data systems, machine learning algorithms, data preprocessing, and model evaluation techniques.

In the case of machine learning roles, take-home assignments regarding modeling, data analysis, and algorithm optimization are incorporated. Apart from these, your proficiency against hypothesis testing, probability distributions, and advanced ML concepts 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 throughout your day at the GoPro office. Your technical prowess, including programming, machine learning modeling capabilities, and problem-solving skills, will be evaluated against other candidates during 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 GoPro.

Quick Tips For GoPro Machine Learning Engineer Interviews

• Brush up on machine learning algorithms, data preprocessing steps, and model evaluation techniques. Mock interview questions on Interview Query can be quite useful.
• Understand GoPro’s product range and think about how machine learning can be applied to improve their offerings.
• Demonstrate a collaborative mindset during behavioral interviews, as GoPro values teamwork and innovation.

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

• Write a query to get the average order value by gender. Given three tables representing customer transactions and customer attributes, write a query to get the average order value by gender. Round the answer to two decimal places.

• 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.

• Find the index where the sum of the left half equals the right half in a list. Given a list of integers, find the index at which the sum of the left half of the list is equal to the right half. If there is no such index, return -1.

• Write a function sorting to sort a list of strings in ascending order from scratch. Given a list of strings, write a function sorting to sort the list in ascending alphabetical order without using the built-in sorted function. Return the new sorted list.

• Write a query to extract the earliest date each user played their third unique song. Given a table of song_plays and a table of users, write a query to extract the earliest date each user played their third unique song. If a user has listened to less than three unique songs, display their name with a NULL date and song name.

• How would you build a model to predict which merchants DoorDash should acquire in a new market? As a data scientist at DoorDash, describe the steps and features you would use to build a predictive model to identify which merchants the company should target for acquisition when entering a new market.

• How would you determine the customer service quality through the chat box for small businesses on Facebook Marketplace? Working at Facebook, your team aims to help small businesses increase sales through the Marketplace app. Explain how you would assess the quality of customer service provided through chat interactions between small businesses and consumers.

• What business health metrics would you track on a dashboard for an e-commerce D2C sock business? If you are in charge of an e-commerce D2C business selling socks, list and explain the key business health metrics you would monitor on a company dashboard to ensure the business is performing well.

• Write a query to determine if user interactions on a website lead to higher purchasing volumes. Given three tables (users, transactions, and events), write a SQL query to analyze whether users who interact on the website (e.g., likes, comments) have a higher purchasing volume compared to those who do not interact.

• How does random forest generate the forest and why use it over logistic regression? Explain the process of generating a forest in a random forest algorithm and discuss the advantages of using random forest over logistic regression.

• How do we deal with missing square footage data to construct a housing price model? You have 100K sold listings over the past three years for Seattle, but 20% are missing square footage data. Describe methods to handle the missing data to build an accurate housing price prediction model.

• How would you build a model to predict which merchants DoorDash should acquire in a new market? As a data scientist at DoorDash, outline the steps to create a model that predicts which merchants to target for acquisition when entering a new market.

• How do you detect and handle correlation between variables in linear regression? Describe methods to detect and manage correlation between variables in a linear regression model. Explain the consequences of ignoring such correlations.

• How would you design a model to detect potential bombs at a border crossing? Outline the design of a model to detect potential bombs at a border crossing, including the selection of inputs and outputs, accuracy measurement, and testing procedures.

• How many more samples are needed to decrease the margin of error from 3 to 0.3? Given a sample size (n) with a margin of error of 3, calculate the additional samples required to reduce the margin of error to 0.3.

• What is the mean and variance of the distribution of (2X - Y)? Given (X) and (Y) are independent random variables with normal distributions (X \sim \mathcal{N}(3, 4)) and (Y \sim \mathcal{N}(1, 4)), determine the mean and variance of (2X - Y).

• How do you calculate the sample size and power for an AB test? For an AB test with a test group and a control group:

• Explain how to calculate the necessary sample size for accurate measurement.
• If the sample size is sufficient, should the power increase or decrease to measure very small differences?

The Machine Learning Engineer position at GoPro is both challenging and rewarding, offering a unique opportunity to work on cutting-edge technology in an innovative environment. If you want more insights about the company, check out our main GoPro 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 GoPro’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 GoPro 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!