GoodRx Machine Learning Engineer Interview Questions + Guide in 2025
Overview
GoodRx is dedicated to making prescriptions more affordable and accessible for all Americans, leveraging technology and data to create innovative solutions in healthcare.
As a Machine Learning Engineer at GoodRx, you will be pivotal in developing and implementing machine learning models and pipelines that directly impact user experiences and business decisions. Key responsibilities include designing analytical capabilities and applications, building production-quality code, and collaborating with data scientists and analysts to translate prototypes into production-ready deployments. You will also work on enhancing infrastructure reliability and efficiency, participating in all phases of the product development life cycle, and leading proof of concept projects.
To excel in this role, you should possess a Master's degree in a related field and have over seven years of experience in building machine learning systems at scale. Proficiency in programming languages such as Python, Scala, or Golang, and substantial experience with cloud platforms (AWS, GCP, Azure) is crucial. Knowledge of machine learning techniques, deep learning frameworks, data engineering practices, and strong SQL skills will set you apart. A solid understanding of software engineering principles and the ability to manage multiple priorities while collaborating with cross-functional teams is essential.
This guide will help you prepare for your interview by providing insights into what GoodRx values in a candidate and the skills and experiences you need to highlight. It is designed to give you a competitive edge in showcasing your qualifications for the role.
What Goodrx Looks for in a Machine Learning Engineer
Goodrx Machine Learning Engineer Interview Process
The interview process for a Machine Learning Engineer at GoodRx is structured to assess both technical and interpersonal skills, ensuring candidates are well-suited for the role and the company culture. The process typically unfolds in several stages:
1. Initial Screening
The process begins with a phone call from a recruiter, lasting about 30 minutes. This initial screening focuses on your background, work experience, and interest in the position. The recruiter will also provide insights into the company and its mission, setting the stage for the subsequent technical evaluations.
2. Technical Assessment
Following the initial screening, candidates may be required to complete a technical assessment, often conducted through platforms like HackerRank. This assessment typically includes SQL queries, Python programming tasks, and possibly feature engineering or model training exercises. Candidates should be prepared to demonstrate their coding skills and problem-solving abilities within a set time limit.
3. Technical Interviews
Candidates who pass the technical assessment will move on to one or more technical interviews. These interviews may involve discussions with engineers or hiring managers, focusing on your previous projects, technical stack, and specific machine learning algorithms. Expect questions that delve into your experience with cloud platforms (AWS, GCP, Azure), data engineering practices, and software development principles.
4. System Design and Behavioral Interviews
In addition to technical interviews, candidates will likely participate in system design interviews where they will be asked to outline their approach to building scalable machine learning systems. Behavioral interviews will also be part of the process, assessing your interpersonal skills, teamwork, and how you handle complex technical challenges. Be prepared to discuss past experiences and how they relate to the responsibilities of the role.
5. Final Interview Round
The final round may involve a more in-depth discussion with senior leadership or cross-functional teams. This stage is designed to evaluate your fit within the company culture and your ability to collaborate with various stakeholders. Candidates may be asked to present a previous project or discuss how they would approach specific business problems.
As you prepare for your interview, consider the types of questions that may arise in each of these stages, particularly those related to algorithms, Python, and machine learning techniques.
Goodrx Machine Learning Engineer Interview Tips
Here are some tips to help you excel in your interview.
Prepare for Technical Depth
Given the emphasis on algorithms and machine learning in this role, ensure you have a solid grasp of various machine learning techniques and algorithms. Be prepared to discuss your experience with specific algorithms such as SVM, k-NN, and regression models. Familiarize yourself with the theoretical underpinnings as well as practical implementations, especially in the context of large-scale systems. You may be asked to explain how you would approach a problem using these techniques, so practice articulating your thought process clearly.
Master the Coding Assessment
Many candidates have reported a coding assessment as part of the interview process. Brush up on your Python skills, as well as SQL, since these are crucial for the role. Practice coding problems that involve data manipulation, feature engineering, and model training. Use platforms like HackerRank or LeetCode to simulate the timed environment you might encounter. Be ready to explain your code and thought process during the assessment, as interviewers often look for clarity and reasoning in addition to correct solutions.
Showcase Your Project Experience
Expect to discuss your previous projects in detail, particularly those that relate to machine learning systems. Be prepared to explain your role, the technologies you used, and the impact of your work. Highlight any experience you have with cloud platforms like AWS, GCP, or Azure, as well as tools like Databricks and Apache Spark. This is your opportunity to demonstrate not just your technical skills, but also your ability to contribute to the team and the company’s mission.
Understand the Company Culture
GoodRx values collaboration and communication, so be prepared for behavioral questions that assess your interpersonal skills. Reflect on your experiences working in teams, managing conflicts, and collaborating with cross-functional stakeholders. Show that you can adapt to different working styles and contribute positively to the team dynamic. Additionally, familiarize yourself with GoodRx’s mission to make prescriptions affordable, as demonstrating alignment with their values can set you apart.
Be Ready for System Design Questions
Given the complexity of machine learning systems, you may encounter system design questions. Practice designing scalable machine learning architectures and be prepared to discuss how you would handle data pipelines, ETL processes, and deployment strategies. Think about how you would ensure reliability and efficiency in your designs, and be ready to discuss trade-offs and decisions you would make in real-world scenarios.
Follow Up Thoughtfully
After your interviews, consider sending a follow-up email to express your gratitude for the opportunity and reiterate your interest in the role. This is also a chance to briefly mention any points you feel you didn’t fully address during the interview. A thoughtful follow-up can leave a positive impression and keep you top of mind as they make their decision.
By focusing on these areas, you can present yourself as a well-rounded candidate who not only possesses the technical skills required for the role but also fits well within the GoodRx culture. Good luck!
Goodrx Machine Learning Engineer Interview Questions
In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at GoodRx. The interview process will likely focus on your technical expertise in machine learning, programming skills, and your ability to work with data systems. Be prepared to discuss your previous projects in detail, as well as demonstrate your problem-solving skills through coding challenges and system design questions.
Machine Learning
1. Can you explain the difference between supervised and unsupervised learning?
Understanding the fundamental concepts of machine learning is crucial. Be clear and concise in your explanation, providing examples of each type of learning.
How to Answer
Discuss the definitions of both supervised and unsupervised learning, highlighting the key differences in how they are used and the types of problems they solve.
Example
“Supervised learning involves training a model on a labeled dataset, 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, where the model tries to identify patterns or groupings, such as clustering customers based on purchasing behavior.”
2. Describe a machine learning project you have worked on. What challenges did you face?
This question assesses your practical experience and problem-solving skills.
How to Answer
Provide a structured overview of the project, focusing on the problem, your approach, the challenges encountered, and the outcomes.
Example
“I worked on a project to predict customer churn for a subscription service. One challenge was dealing with imbalanced data, which I addressed by implementing SMOTE for oversampling. The model ultimately improved retention rates by 15%.”
3. What machine learning algorithms are you most familiar with, and when would you use them?
This question tests your knowledge of various algorithms and their applications.
How to Answer
List the algorithms you are comfortable with and provide scenarios where each would be applicable.
Example
“I am familiar with decision trees, SVM, and k-NN. I would use decision trees for interpretability in business contexts, SVM for high-dimensional data classification, and k-NN for recommendation systems due to its simplicity and effectiveness.”
4. How do you handle overfitting in your models?
This question evaluates your understanding of model performance and generalization.
How to Answer
Discuss techniques you use to prevent overfitting, such as regularization, cross-validation, or pruning.
Example
“To combat overfitting, I often use techniques like L1 and L2 regularization to penalize large coefficients. Additionally, I implement cross-validation to ensure the model performs well on unseen data.”
Programming and Technical Skills
1. What is your experience with Python for machine learning?
This question assesses your programming skills and familiarity with relevant libraries.
How to Answer
Discuss your experience with Python and specific libraries you have used for machine learning.
Example
“I have extensive experience using Python for machine learning, particularly with libraries like scikit-learn for model building and pandas for data manipulation. I also utilize TensorFlow for deep learning projects.”
2. Can you write a SQL query to find the top 10 customers by total spend?
This question tests your SQL skills, which are essential for data manipulation.
How to Answer
Provide a clear SQL query that accomplishes the task, explaining your thought process.
Example
“Sure, the SQL query would look like this:
sql
SELECT customer_id, SUM(spend) AS total_spend
FROM transactions
GROUP BY customer_id
ORDER BY total_spend DESC
LIMIT 10;
This query aggregates the total spend per customer and orders the results to show the top spenders.”
3. Describe your experience with cloud platforms like AWS or GCP.
This question evaluates your familiarity with cloud services used for deploying machine learning models.
How to Answer
Discuss specific services you have used and how they contributed to your projects.
Example
“I have worked extensively with AWS, utilizing services like S3 for data storage and SageMaker for building and deploying machine learning models. This allowed for scalable training and easy integration with other AWS services.”
4. How do you approach debugging a machine learning model?
This question assesses your problem-solving skills and understanding of model performance.
How to Answer
Explain your systematic approach to identifying and resolving issues in model performance.
Example
“When debugging a model, I start by analyzing the data for inconsistencies or outliers. Then, I check the model’s assumptions and performance metrics. If necessary, I iterate on feature selection and model parameters to improve results.”
System Design and Architecture
1. How would you design a machine learning pipeline for a new feature?
This question tests your ability to think critically about system architecture.
How to Answer
Outline the steps you would take to design a robust machine learning pipeline, including data collection, preprocessing, model training, and deployment.
Example
“I would start by defining the problem and identifying the data sources. Next, I would implement data preprocessing steps, followed by model training using a suitable algorithm. Finally, I would deploy the model using a microservice architecture, ensuring it can scale and integrate with existing systems.”
2. Explain the concept of feature engineering and its importance.
This question evaluates your understanding of data preparation for machine learning.
How to Answer
Discuss what feature engineering is and why it is critical for model performance.
Example
“Feature engineering involves creating new input features from raw data to improve model performance. It’s crucial because the right features can significantly enhance the model’s ability to learn patterns and make accurate predictions.”
3. What strategies do you use for model evaluation?
This question assesses your knowledge of model validation techniques.
How to Answer
Discuss the metrics and methods you use to evaluate model performance.
Example
“I use metrics like accuracy, precision, recall, and F1-score for classification tasks, and RMSE for regression. I also implement cross-validation to ensure the model’s robustness across different data splits.”
4. How do you ensure the scalability of your machine learning solutions?
This question tests your understanding of deploying machine learning models in production.
How to Answer
Discuss the practices you follow to ensure that your solutions can handle increased loads.
Example
“I ensure scalability by designing models that can be easily distributed across multiple nodes and using cloud services that allow for dynamic resource allocation. Additionally, I monitor performance and optimize the codebase regularly.”