Strava Machine Learning Engineer Interview Questions + Guide in 2025

Strava is a leading digital community for athletes, connecting over 125 million active individuals worldwide to enhance their fitness journeys.

As a Machine Learning Engineer at Strava, you will play a pivotal role within the AI and Machine Learning team, focusing on developing and deploying machine learning models that enhance user experiences and drive growth across the platform. Key responsibilities include end-to-end management of the ML pipeline, from prototyping and building models to deploying them into production and constructing data pipelines. You will collaborate closely with product teams to ensure that your projects align with user needs and contribute to the overall product vision.

To thrive in this role, you should possess a strong foundation in algorithms and machine learning principles, alongside proficiency in programming languages such as Python. Experience in building scalable ML models for production use is crucial, as well as familiarity with data pipeline technologies. Your ability to work collaboratively and mentor fellow engineers will also be instrumental in fostering a positive team culture that embodies Strava's values of inclusivity and growth.

This guide aims to equip you with the insights and knowledge necessary to excel in your interview for the Machine Learning Engineer position at Strava. By understanding the responsibilities and expectations of the role, you will be better prepared to demonstrate how your skills and experiences align with Strava's mission and values.

The interview process for the Machine Learning Engineer role at Strava is designed to assess both technical expertise and cultural fit within the team. Here’s what you can expect:

1. Initial Recruiter Call

The first step in the interview process is a 30-minute phone call with a recruiter. This conversation will focus on your background, experiences, and motivations for applying to Strava. The recruiter will also provide insights into the company culture and the specifics of the Machine Learning Engineer role, ensuring that you understand the expectations and responsibilities involved.

2. Technical Assessment

Following the initial call, candidates typically undergo a technical assessment, which may be conducted via a coding platform or through a video call. This assessment will evaluate your proficiency in algorithms and machine learning concepts, as well as your coding skills in Python. Expect to solve problems that require you to demonstrate your understanding of model prototyping, data analysis, and the application of machine learning techniques.

3. Onsite Interviews

The onsite interview consists of multiple rounds, usually around four to five, where you will meet with various team members, including other machine learning engineers and product managers. Each interview will last approximately 45 minutes and will cover a mix of technical and behavioral questions. You will be asked to discuss your previous projects, particularly those involving the end-to-end development of machine learning models, and how you have collaborated with cross-functional teams. Additionally, expect to engage in discussions about your experience with data pipelines and operational excellence in machine learning.

4. Final Interview with Leadership

The final step in the process is typically an interview with a senior leader or manager from the AI and Machine Learning team. This conversation will focus on your long-term vision, alignment with Strava's mission, and how you can contribute to the team’s goals. It’s an opportunity for you to showcase your passion for the role and the impact you hope to make within the company.

As you prepare for these interviews, it’s essential to reflect on your experiences and be ready to discuss how they align with Strava's values and the specific requirements of the Machine Learning Engineer role. Next, let’s delve into the types of questions you might encounter during the interview process.

Here are some tips to help you excel in your interview.

Embrace the Intersection of AI and Fitness

Strava is passionate about connecting athletes with their motivations, and as a Machine Learning Engineer, you will be at the forefront of this mission. Show your enthusiasm for both AI and fitness during the interview. Discuss how your personal interests align with Strava’s goals and how you envision using machine learning to enhance user experiences. This connection will demonstrate your commitment to the role and the company’s mission.

Showcase Your End-to-End ML Experience

The role requires you to own projects from model prototyping to deployment. Be prepared to discuss specific examples of ML models you have built and shipped in production. Highlight your experience with the entire ML pipeline, including data collection, exploratory data analysis, model training, and performance monitoring. This will illustrate your capability to contribute effectively to Strava’s AI and ML team.

Highlight Collaboration and Mentorship

Strava values a collaborative team culture. Be ready to share experiences where you worked closely with product teams or mentored junior engineers. Discuss how you fostered a positive team environment and contributed to collective success. This will show that you not only possess technical skills but also align with Strava’s emphasis on teamwork and inclusivity.

Prepare for Technical Proficiency

Given the importance of algorithms and Python in this role, ensure you are well-versed in these areas. Brush up on your knowledge of machine learning frameworks like Scikit-learn and PyTorch, and be ready to discuss your experience with big data technologies such as Spark and SQL. You may be asked to solve technical problems or discuss your approach to building data pipelines, so practice articulating your thought process clearly.

Cultivate a Growth Mindset

Strava appreciates candidates who are proactive about learning and adapting. Be prepared to discuss how you stay current with industry trends and technologies. Share examples of how you have sought out new skills or tackled challenges in your previous roles. This will demonstrate your commitment to personal and professional growth, which is highly valued at Strava.

Align with Strava’s Values

Strava is committed to diversity, inclusion, and creating a positive workplace culture. Familiarize yourself with their values and be ready to discuss how you can contribute to an inclusive environment. Share your thoughts on the importance of diversity in tech and how you have supported these initiatives in your past experiences.

Ask Insightful Questions

Prepare thoughtful questions that reflect your understanding of Strava’s mission and the role. Inquire about the team’s current projects, challenges they face, or how they measure the success of their ML models. This not only shows your genuine interest in the position but also allows you to assess if Strava is the right fit for you.

By following these tips, you will be well-prepared to make a strong impression during your interview at Strava. Good luck!

Strava Machine Learning Engineer Interview Questions

In this section, we’ll review the various interview questions that might be asked during a Strava machine learning engineer interview. The interview will focus on your technical expertise in machine learning, algorithms, and data processing, as well as your ability to work collaboratively within a team. Be prepared to discuss your experience with model development, deployment, and operational excellence.

Machine Learning

1. Can you explain the difference between supervised and unsupervised learning?

Understanding the fundamental concepts of machine learning is crucial for this role, as it will help you articulate your approach to model development.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight scenarios where you would choose one approach over the other.

Example

“Supervised learning involves training a model on labeled data, where the outcome is known, such as predicting house prices based on features like size and location. In contrast, unsupervised learning deals with unlabeled data, aiming to find hidden patterns, like clustering customers based on purchasing behavior.”

2. Describe a machine learning project you have worked on from start to finish.

This question assesses your hands-on experience and ability to manage projects end-to-end.

How to Answer

Outline the project’s objectives, the data you used, the models you built, and the results achieved. Emphasize your role in the project and any challenges you overcame.

Example

“I worked on a recommendation system for a fitness app. I started with exploratory data analysis to understand user behavior, then built a collaborative filtering model using Python and Scikit-learn. After deploying the model, we saw a 20% increase in user engagement within the first month.”

3. How do you handle overfitting in your models?

Overfitting is a common issue in machine learning, and interviewers want to know your strategies for mitigating it.

How to Answer

Discuss techniques such as cross-validation, regularization, and pruning. Provide examples of how you have applied these methods in your work.

Example

“To combat overfitting, I often use cross-validation to ensure my model generalizes well to unseen data. Additionally, I apply L1 or L2 regularization to penalize overly complex models, which helps maintain a balance between bias and variance.”

4. What metrics do you use to evaluate the performance of a machine learning model?

Understanding model evaluation is key to ensuring the effectiveness of your solutions.

How to Answer

Mention various metrics relevant to the type of model you are discussing, such as accuracy, precision, recall, F1 score, and AUC-ROC. Explain why you would choose specific metrics based on the problem context.

Example

“For classification tasks, I typically use accuracy and F1 score to evaluate model performance, as they provide insights into both the overall correctness and the balance between precision and recall. For regression tasks, I prefer metrics like RMSE or MAE to assess prediction errors.”

Algorithms

1. Can you explain a common algorithm you have implemented and its use case?

This question tests your knowledge of algorithms and their practical applications.

How to Answer

Choose an algorithm you are familiar with, explain how it works, and describe a specific use case where you applied it.

Example

“I have implemented the Random Forest algorithm for a customer segmentation project. It works by constructing multiple decision trees and averaging their predictions to improve accuracy and control overfitting. This approach helped us identify distinct customer groups based on purchasing patterns.”

2. How do you optimize hyperparameters in your models?

Hyperparameter tuning is essential for improving model performance, and interviewers want to know your approach.

How to Answer

Discuss methods such as grid search, random search, or Bayesian optimization. Provide an example of how you have successfully optimized hyperparameters in a past project.

Example

“I typically use grid search combined with cross-validation to find the best hyperparameters for my models. For instance, in a recent project, I optimized the learning rate and the number of estimators for a gradient boosting model, which resulted in a significant improvement in accuracy.”

3. What is your experience with ensemble methods?

Ensemble methods can enhance model performance, and your familiarity with them is important for this role.

How to Answer

Explain what ensemble methods are, the types you have used, and the benefits they provide. Share a specific example of how you applied them.

Example

“I have experience with both bagging and boosting ensemble methods. For example, I used AdaBoost to improve the performance of a weak classifier in a fraud detection system, which led to a 15% increase in detection rates compared to using a single model.”

Data Processing

1. How do you approach exploratory data analysis (EDA)?

EDA is a critical step in the data science process, and interviewers want to know your methodology.

How to Answer

Describe the steps you take during EDA, including data cleaning, visualization, and feature selection. Highlight the tools you use.

Example

“I start EDA by cleaning the data to handle missing values and outliers. Then, I use visualization tools like Matplotlib and Seaborn to identify patterns and correlations. This process helps me select relevant features for model training.”

2. What experience do you have with data pipelines?

Data pipelines are essential for processing and transforming data for machine learning models.

How to Answer

Discuss your experience with building and maintaining data pipelines, including the technologies you have used.

Example

“I have built data pipelines using Apache Spark and AWS Glue to process large datasets efficiently. This involved extracting data from various sources, transforming it for analysis, and loading it into a data warehouse for model training.”

3. Can you explain the importance of feature engineering?

Feature engineering can significantly impact model performance, and interviewers want to gauge your understanding of its importance.

How to Answer

Discuss how feature engineering can improve model accuracy and the techniques you use to create new features.

Example

“Feature engineering is crucial because it allows us to create informative features that can enhance model performance. For instance, in a time series analysis, I created lag features and rolling averages to capture trends and seasonality, which improved the model’s predictive power.”