Affirm, Inc. Machine Learning Engineer Interview Questions + Guide in 2025

Affirm, Inc. is revolutionizing the credit landscape by offering transparent financial solutions that empower consumers to buy now and pay later without hidden fees or compounding interest.

As a Machine Learning Engineer at Affirm, you will play a pivotal role in developing and deploying machine learning models that enhance the company's core services, particularly in assessing creditworthiness, detecting fraud, and personalizing shopping experiences. Your key responsibilities will include utilizing proprietary and third-party data to create models that predict outcomes like fraud likelihood and credit risk, collaborating with cross-functional teams to define product requirements, and building the infrastructure necessary for scalable machine learning applications. Ideal candidates will possess strong programming skills in Python, experience with large-scale distributed systems such as Spark or Ray, and a deep understanding of machine learning frameworks. Furthermore, excellent communication skills are essential, as you will be expected to present complex technical concepts to diverse audiences.

This guide will help you prepare for your interview by providing insights into the specific skills and experiences that Affirm values in a Machine Learning Engineer. With a focused understanding of the role's expectations, you'll be well-equipped to showcase your qualifications and align your responses with the company's mission and values.

The interview process for a Machine Learning Engineer at Affirm is structured and thorough, typically consisting of multiple stages designed to assess both technical and interpersonal skills.

1. Initial Recruiter Call

The process begins with a 30-minute phone call with a recruiter. This conversation serves as an introduction to the role and the company culture. The recruiter will inquire about your background, relevant experiences, and career aspirations. This is also an opportunity for you to ask questions about the company and the position.

2. Hiring Manager Interview

Following the initial call, candidates usually have a discussion with the hiring manager. This interview focuses on your technical expertise and how your experience aligns with the team's needs. Expect to discuss specific projects you've worked on, your approach to problem-solving, and how you would handle real-world scenarios relevant to Affirm's business model. The hiring manager may also assess your ability to communicate complex concepts clearly.

3. Technical Interviews

Candidates typically undergo two technical rounds. The first round often involves feature engineering tasks where you will be asked to work with an existing dataset. This will test your ability to extract meaningful features that can improve model performance. The second round usually consists of a coding challenge, where you will solve a problem that reflects the type of work you would be doing at Affirm. The focus here is on practical application rather than abstract coding challenges.

4. Final Interview Round

The final stage may include additional technical assessments or a panel interview with team members. This round is designed to evaluate your fit within the team and your ability to collaborate effectively. You may be asked to present your past work or discuss how you would approach specific challenges related to machine learning and data processing at Affirm.

As you prepare for your interview, it's essential to be ready for a variety of questions that will test your technical knowledge and problem-solving skills.

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

Understand the Interview Process

The interview process at Affirm can be lengthy, often involving multiple stages. Be prepared for a thorough evaluation that may include technical assessments, behavioral interviews, and discussions with various team members. Familiarize yourself with the structure of the interview and the types of questions you might encounter. This will help you manage your time and energy effectively throughout the process.

Tailor Your Experience to Affirm's Needs

During your interviews, be ready to discuss your previous experiences in a way that aligns with Affirm's mission and the specific role. Highlight your work with machine learning models, particularly in areas like fraud detection, credit assessment, or personalized user experiences. Use concrete examples to demonstrate how your skills can contribute to Affirm's goals, such as improving user trust and optimizing financial decisions.

Prepare for Technical Challenges

Expect technical rounds that focus on real-world applications of machine learning. Brush up on feature engineering, model evaluation, and coding challenges relevant to the role. Practice solving problems that are similar to those you might face at Affirm, such as developing pipelines for fraud detection or optimizing algorithms for credit scoring. Familiarity with tools like Python, Spark, and machine learning frameworks will be crucial.

Communicate Effectively

Affirm values strong communication skills, so be prepared to articulate your thought process clearly. When discussing technical concepts, tailor your explanations to your audience, whether they are technical or non-technical stakeholders. Demonstrating your ability to collaborate with cross-functional teams will be key, so share examples of how you've successfully worked with product and engineering teams in the past.

Embrace the Company Culture

Affirm prides itself on a culture of transparency, responsibility, and innovation. Show that you resonate with these values by discussing how you approach challenges with persistence and a sense of ownership. Be genuine in your interactions, and express your enthusiasm for contributing to a company that aims to make credit more honest and accessible.

Ask Insightful Questions

Prepare thoughtful questions that reflect your understanding of Affirm's business model and the role of machine learning within it. Inquire about the challenges the team is currently facing, the technologies they are using, or how they measure the success of their models. This not only shows your interest in the position but also helps you assess if Affirm is the right fit for you.

By following these tips, you can present yourself as a strong candidate who is well-prepared to contribute to Affirm's mission and thrive in their dynamic environment. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Affirm. The interview process is designed to assess both technical skills and the ability to apply machine learning concepts to real-world problems, particularly in the context of fraud detection and creditworthiness assessment. Candidates should be prepared to demonstrate their knowledge of machine learning algorithms, data manipulation, and their experience in building production-ready models.

Machine Learning Concepts

1. Can you explain how a logistic regression model works and how you would interpret its predictions?

Understanding logistic regression is fundamental, as it is often used in binary classification tasks. Be prepared to discuss the model's mechanics and how to interpret coefficients and probabilities.

How to Answer

Explain the logistic function and how it transforms linear combinations of input features into probabilities. Discuss how to interpret the coefficients in terms of odds ratios.

Example

“A logistic regression model predicts the probability of a binary outcome based on one or more predictor variables. The output is a value between 0 and 1, which can be interpreted as the likelihood of the positive class. For instance, if a coefficient is positive, it indicates that as the predictor increases, the odds of the outcome occurring also increase.”

2. Describe a machine learning project you worked on that involved feature engineering. What techniques did you use?

Feature engineering is crucial for model performance. Discuss your experience with transforming raw data into meaningful features.

How to Answer

Detail the specific techniques you used, such as normalization, encoding categorical variables, or creating interaction terms. Highlight the impact of these features on model performance.

Example

“In a project predicting loan defaults, I created features such as the ratio of debt to income and the number of previous loans. I also applied one-hot encoding to categorical variables like employment status. This feature engineering significantly improved the model's accuracy.”

3. How do you handle imbalanced datasets in machine learning?

Imbalanced datasets can skew model performance. Be prepared to discuss strategies for addressing this issue.

How to Answer

Mention techniques such as resampling methods (oversampling/undersampling), using different evaluation metrics, or applying algorithms that are robust to class imbalance.

Example

“To address class imbalance in a fraud detection model, I used SMOTE to oversample the minority class. Additionally, I focused on precision and recall metrics rather than accuracy to better evaluate the model's performance.”

4. What is the difference between supervised and unsupervised learning? Can you provide examples of each?

This question tests your foundational knowledge of machine learning paradigms.

How to Answer

Define both terms clearly and provide relevant examples that illustrate their differences.

Example

“Supervised learning involves training a model on labeled data, such as predicting loan defaults based on historical data. In contrast, unsupervised learning deals with unlabeled data, like clustering customers based on purchasing behavior without predefined categories.”

Data Manipulation and Programming

1. How would you implement a data pipeline for a machine learning model?

Discussing data pipelines is essential, as they are critical for model deployment and maintenance.

How to Answer

Outline the steps involved in creating a data pipeline, including data ingestion, preprocessing, feature extraction, and model training.

Example

“I would use Apache Airflow to orchestrate the data pipeline, starting with data ingestion from various sources. Then, I would preprocess the data using Pandas, followed by feature extraction and finally training the model using Scikit-learn.”

2. Can you explain how you would optimize a machine learning model?

Model optimization is key to improving performance. Be ready to discuss techniques you have used.

How to Answer

Mention methods such as hyperparameter tuning, feature selection, and cross-validation.

Example

“I typically use Grid Search for hyperparameter tuning, combined with cross-validation to ensure that the model generalizes well to unseen data. Additionally, I analyze feature importance to eliminate irrelevant features that may introduce noise.”

3. What programming languages and libraries are you proficient in for machine learning?

This question assesses your technical skills and familiarity with relevant tools.

How to Answer

List the programming languages and libraries you have experience with, emphasizing their application in machine learning.

Example

“I am proficient in Python and frequently use libraries such as Scikit-learn for model building, Pandas for data manipulation, and NumPy for numerical computations. I also have experience with PyTorch for deep learning applications.”

4. Describe a coding challenge you faced in a machine learning project and how you resolved it.

This question evaluates your problem-solving skills in a technical context.

How to Answer

Share a specific challenge, the steps you took to resolve it, and the outcome.

Example

“During a project, I encountered a memory issue while processing a large dataset. I resolved it by optimizing my data loading process using Dask, which allowed me to handle larger-than-memory datasets efficiently.”

Domain Knowledge

1. What are some common techniques for fraud detection using machine learning?

This question assesses your understanding of the specific domain relevant to Affirm.

How to Answer

Discuss various techniques and algorithms that are effective in detecting fraudulent activities.

Example

“Common techniques for fraud detection include anomaly detection using clustering algorithms, supervised learning models like decision trees, and ensemble methods such as random forests. Each method can be tailored to identify unusual patterns indicative of fraud.”

2. How do you assess the creditworthiness of a borrower using machine learning?

This question tests your ability to apply machine learning in the context of credit assessment.

How to Answer

Explain the factors considered in credit assessment and how machine learning models can be used to evaluate them.

Example

“I assess creditworthiness by developing models that analyze historical repayment behavior, income levels, and credit history. By training models on these features, I can predict the likelihood of default and make informed lending decisions.”

3. Can you discuss the importance of explainability in machine learning models, especially in financial applications?

Explainability is crucial in finance due to regulatory requirements and consumer trust.

How to Answer

Discuss the need for transparency in model predictions and the methods used to achieve it.

Example

“In financial applications, explainability is vital for compliance and customer trust. I use techniques like LIME or SHAP to provide insights into model predictions, ensuring stakeholders understand the rationale behind decisions.”

4. What role does data privacy play in developing machine learning models for financial services?

Data privacy is a significant concern in finance. Be prepared to discuss its implications.

How to Answer

Highlight the importance of data protection regulations and ethical considerations in model development.

Example

“Data privacy is paramount in financial services. I ensure compliance with regulations like GDPR by anonymizing sensitive data and implementing strict access controls, which helps maintain customer trust while developing effective models.”