Hims & Hers Machine Learning Engineer Interview Questions + Guide in 2025

Hims & Hers is a multi-specialty telehealth platform focused on providing high-quality medical care through a virtual interface, addressing various health challenges to empower individuals in their wellness journeys.

As a Machine Learning Engineer at Hims & Hers, you will play a pivotal role in leveraging data to develop innovative healthcare solutions. Your key responsibilities will include collaborating with product managers, engineers, and designers to create machine-learning systems that enhance user access to care. You will guide data-driven decisions, utilizing your extensive knowledge of statistical applications and machine learning tools to solve real-world problems, such as building recommendation systems and predictive models. A strong background in machine learning fundamentals, along with 3-5+ years of experience in data science or machine learning, is essential. Your success in this role will also depend on your ability to mentor others, contribute to team retrospectives, and participate actively in continuous process improvements.

This guide is designed to help you prepare effectively for your interview by providing insights into the skills and experiences that align with Hims & Hers' mission and values.

The interview process for a Machine Learning Engineer at Hims & Hers is structured to assess both technical skills and cultural fit within the organization. The process typically unfolds in several stages:

1. Initial Screening

The first step is a phone interview with a recruiter, lasting approximately 20-30 minutes. During this call, the recruiter will discuss the role, the company culture, and your background. Expect questions about your motivation for applying to Hims & Hers and your relevant experiences. This is also an opportunity for you to ask questions about the company and the position.

2. Technical Screen

Following the initial screening, candidates usually participate in a technical interview, which may be conducted via video call. This session typically lasts around 45 minutes and focuses on your technical expertise in machine learning and data science. You may be asked to solve a problem or discuss your past projects, particularly those involving algorithms, Python, and machine learning techniques. Be prepared to explain your thought process and approach to problem-solving.

3. Managerial Interview

Next, candidates will have a conversation with the hiring manager. This interview is designed to assess your fit within the team and your ability to collaborate with product managers and engineers. Expect questions about your experience working with cross-functional teams and how you approach mentoring and continuous improvement in your work.

4. Panel Interviews

The panel interview stage typically consists of multiple rounds with different team members, including engineers and possibly other stakeholders. Each interview lasts about 30-45 minutes and may cover both technical and behavioral questions. You should be ready to discuss your experience with machine learning tools, statistical modeling, and specific projects you've worked on. The STAR method (Situation, Task, Action, Result) is often encouraged for behavioral questions.

5. Final Interview

The final round usually involves a discussion with executive management. This interview focuses on your long-term vision, alignment with the company's mission, and how you can contribute to Hims & Hers' goals. It may also include a case study or a presentation where you demonstrate your problem-solving skills and strategic thinking.

Throughout the process, candidates are encouraged to showcase their knowledge of machine learning fundamentals and their ability to apply these concepts to real-world problems.

Now that you have an understanding of the interview process, let's delve into the specific questions that candidates have encountered during their interviews.

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

Understand the Company’s Mission and Values

Hims & Hers is dedicated to breaking down barriers to healthcare access and normalizing health challenges. Familiarize yourself with their mission and how they aim to innovate in the telehealth space. Be prepared to articulate how your personal values align with their mission and how you can contribute to their goals. This will not only demonstrate your enthusiasm for the role but also show that you are a good cultural fit.

Prepare for Behavioral Questions

Expect a significant focus on behavioral questions during your interviews. Use the STAR (Situation, Task, Action, Result) method to structure your responses. Reflect on your past experiences, particularly those that highlight your collaboration with product managers and engineers, as well as your ability to mentor others. Be ready to discuss specific projects where you successfully implemented machine learning solutions and how those impacted product metrics.

Brush Up on Technical Skills

Given the emphasis on algorithms and machine learning in this role, ensure you are well-versed in relevant technical skills. Review your knowledge of algorithms, Python, and machine learning fundamentals. Be prepared to discuss your experience with statistical modeling techniques and how you have applied them in real-world scenarios. Additionally, practice coding problems and be ready to explain your thought process clearly during technical interviews.

Showcase Your Problem-Solving Abilities

During technical interviews, you may be asked to solve problems related to machine learning systems or data-driven products. Practice articulating your thought process as you work through these problems. Be prepared to discuss how you would approach building a recommendation system or a prediction model, and be ready to explain your reasoning behind the choices you make.

Engage with Your Interviewers

Throughout the interview process, engage with your interviewers by asking insightful questions about their work and the team dynamics. This not only shows your interest in the role but also helps you gauge if the company culture aligns with your expectations. Inquire about the types of projects you would be working on and how the team collaborates to achieve their goals.

Be Authentic and Personable

While technical skills are crucial, Hims & Hers values a warm and inclusive culture. Be yourself during the interviews and let your personality shine through. Share your passion for machine learning and how it can be applied to improve healthcare access. Authenticity can set you apart from other candidates and help you connect with your interviewers on a personal level.

Follow Up Thoughtfully

After your interviews, send a thoughtful follow-up message to express your gratitude for the opportunity to interview. Mention specific topics discussed during the interview that resonated with you. This not only reinforces your interest in the role but also leaves a positive impression on your interviewers.

By following these tips, you can position yourself as a strong candidate for the Machine Learning Engineer role at Hims & Hers. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Hims & Hers. The interview process will likely focus on your technical expertise in machine learning, algorithms, and your ability to collaborate with cross-functional teams. Be prepared to discuss your past experiences, problem-solving skills, and how you can contribute to the company's mission of improving access to healthcare through data-driven solutions.

Machine Learning

1. Can you describe a machine learning project you worked on from start to finish?

This question aims to assess your practical experience with machine learning projects and your ability to manage the entire lifecycle of a project.

How to Answer

Discuss the problem you were trying to solve, the data you used, the algorithms you implemented, and the results you achieved. Highlight your role in the project and any challenges you faced.

Example

“I worked on a project to develop a recommendation system for a healthcare app. I started by gathering user data and cleaning it for analysis. I implemented collaborative filtering algorithms and tested various models to optimize accuracy. The final model improved user engagement by 30%, which was a significant win for the product team.”

2. What machine learning algorithms are you most familiar with, and when would you use them?

This question evaluates your understanding of different algorithms and their applications.

How to Answer

Mention specific algorithms, such as decision trees, random forests, or neural networks, and explain the scenarios in which you would choose each one.

Example

“I am well-versed in decision trees and random forests. I typically use decision trees for simpler problems where interpretability is key, while I opt for random forests when I need to improve accuracy and reduce overfitting. For complex tasks like image recognition, I prefer using convolutional neural networks.”

3. How do you handle overfitting in your models?

This question tests your knowledge of model evaluation and improvement techniques.

How to Answer

Discuss techniques such as cross-validation, regularization, or pruning that you use to prevent overfitting.

Example

“To handle overfitting, I often use cross-validation to ensure my model generalizes well to unseen data. Additionally, I apply regularization techniques like L1 and L2 to penalize overly complex models. In one project, I reduced overfitting by implementing dropout layers in a neural network, which significantly improved performance on validation data.”

4. Describe a time when you had to explain a complex machine learning concept to a non-technical audience.

This question assesses your communication skills and ability to convey technical information clearly.

How to Answer

Provide an example where you simplified a complex concept and tailored your explanation to the audience's level of understanding.

Example

“I once had to explain the concept of a neural network to a group of marketing professionals. I used analogies related to how the human brain processes information and visual aids to illustrate how data flows through layers. By relating it to their experiences, they grasped the concept and its relevance to our marketing strategies.”

Algorithms

1. What is your approach to selecting the right algorithm for a given problem?

This question evaluates your analytical skills and understanding of algorithm selection.

How to Answer

Discuss the factors you consider, such as data type, problem complexity, and performance metrics.

Example

“I start by analyzing the data characteristics, such as size and type. For structured data, I might consider algorithms like logistic regression or decision trees. For unstructured data, I would lean towards neural networks. I also evaluate the problem's complexity and the need for interpretability, which influences my choice of algorithm.”

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

This question tests your foundational knowledge of machine learning concepts.

How to Answer

Clearly define both terms and provide examples of each.

Example

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

3. How do you evaluate the performance of your machine learning models?

This question assesses your understanding of model evaluation metrics.

How to Answer

Discuss various metrics you use, such as accuracy, precision, recall, F1 score, or AUC-ROC, and explain when to use each.

Example

“I evaluate model performance using accuracy for balanced datasets, but I prefer precision and recall for imbalanced datasets to ensure the model is not biased towards the majority class. For binary classification tasks, I often use the F1 score to balance precision and recall.”

4. Describe a time when you had to optimize an algorithm for better performance.

This question looks for your problem-solving skills and experience with optimization.

How to Answer

Share a specific instance where you improved an algorithm's performance and the methods you used.

Example

“In a project where I implemented a recommendation system, I noticed that the initial algorithm was slow due to the large dataset. I optimized it by using matrix factorization techniques, which reduced computation time by 50% while maintaining accuracy, allowing for real-time recommendations.”

Statistics & Probability

1. How do you apply statistical methods in your machine learning projects?

This question evaluates your understanding of the role of statistics in machine learning.

How to Answer

Discuss specific statistical techniques you use and how they inform your modeling decisions.

Example

“I frequently use statistical methods like hypothesis testing to validate assumptions about my data. For instance, I applied A/B testing to determine the effectiveness of a new feature in our app, which helped us make data-driven decisions about its rollout.”

2. Can you explain the concept of p-values and their significance in hypothesis testing?

This question tests your foundational knowledge of statistics.

How to Answer

Define p-values and explain their role in determining statistical significance.

Example

“A p-value indicates the probability of observing the data, or something more extreme, assuming the null hypothesis is true. A low p-value suggests that we can reject the null hypothesis, indicating that our findings are statistically significant. In my previous project, I used p-values to assess the impact of a new marketing strategy on user engagement.”

3. What is the Central Limit Theorem, and why is it important?

This question assesses your understanding of fundamental statistical concepts.

How to Answer

Explain the theorem and its implications for statistical inference.

Example

“The Central Limit Theorem states that the distribution of sample means approaches a normal distribution as the sample size increases, regardless of the population's distribution. This is crucial because it allows us to make inferences about population parameters using sample statistics, which is a common practice in machine learning.”

4. How do you handle missing data in your datasets?

This question evaluates your data preprocessing skills.

How to Answer

Discuss the techniques you use to address missing data, such as imputation or removal.

Example

“I handle missing data by first assessing the extent and pattern of the missingness. If it's minimal, I might use mean or median imputation. For larger gaps, I consider using predictive modeling to estimate missing values or, if appropriate, removing the affected records to maintain data integrity.”