One Medical Machine Learning Engineer Interview Questions + Guide in 2025

One Medical is a tech-driven primary care organization that aims to enhance the patient experience by integrating technology and personalized care.

As a Machine Learning Engineer at One Medical, you will play a pivotal role in developing and implementing machine learning models to enhance healthcare delivery and improve patient outcomes. Key responsibilities include designing algorithms that analyze healthcare data, optimizing existing models for performance, and collaborating with cross-functional teams to integrate these models into production systems. A strong foundation in SQL and algorithms is essential, as you will be tasked with refactoring existing implementations and adding new features to existing systems. Being proficient in testing and debugging is crucial, as is having a solid understanding of system design to ensure scalability and reliability.

The ideal candidate will have experience in applying machine learning techniques to real-world problems, along with an ability to communicate complex technical concepts to non-technical stakeholders. A passion for improving healthcare through technology and a commitment to One Medical’s mission of patient-centered care will set you apart.

This guide will help you prepare for your interview by providing insights into the expectations and focus areas for the Machine Learning Engineer role at One Medical, enabling you to showcase your skills effectively.

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

1. Initial Call with Hiring Manager

The first step is a call with the hiring manager, which serves as an opportunity to gauge mutual fit. This conversation focuses on your background, experiences, and understanding of the role, as well as an overview of One Medical's mission and values. The hiring manager will assess your alignment with the company's culture and your potential contributions to the team.

2. Technical Phone Interview

Following the initial call, candidates participate in a 75-minute technical phone interview. This interview is divided into a 60-minute technical assessment and a 15-minute Q&A session. During this technical interview, candidates are presented with real-world engineering tasks rather than traditional computer science algorithm questions. You may be asked to refactor existing implementations, add new features, and test those features, allowing the interviewers to evaluate your practical coding skills and problem-solving abilities.

3. Onsite Interview

The final stage of the interview process is an onsite interview, which typically lasts around four hours. This comprehensive session includes multiple components: a deep dive into your background and experiences, an assessment of cultural fit, and a system design interview. The deep dive allows interviewers to explore your past projects and contributions in detail, while the cultural fit segment assesses how well you align with One Medical's values and work environment. The system design interview evaluates your ability to architect scalable and efficient machine learning systems, focusing on your thought process and design choices.

As you prepare for the interview, it's essential to familiarize yourself with the types of questions that may arise during these stages.

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at One Medical. The interview process will focus on your technical skills, problem-solving abilities, and cultural fit within the organization. Be prepared to demonstrate your knowledge in machine learning concepts, system design, and practical engineering tasks.

Technical Skills

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

Understanding the fundamental concepts of machine learning is crucial for this role.

How to Answer

Discuss the definitions of both supervised and unsupervised learning, providing examples of each. Highlight the types of problems each approach is best suited for.

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 or groupings, like clustering customers based on purchasing behavior.”

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

This question assesses your practical experience and ability to manage a project lifecycle.

How to Answer

Outline the problem you were solving, the data you used, the model you chose, and the results you achieved. Emphasize your role in the project.

Example

“I worked on a project to predict patient readmission rates. I gathered historical patient data, performed feature engineering, and selected a random forest model. After training and validating the model, we achieved an accuracy of 85%, which helped the hospital implement targeted interventions.”

System Design

3. How would you design a recommendation system for a healthcare application?

This question evaluates your system design skills and understanding of user needs.

How to Answer

Discuss the components of a recommendation system, including data sources, algorithms, and user feedback mechanisms. Consider the unique aspects of healthcare.

Example

“I would start by collecting user data, such as medical history and preferences. I’d use collaborative filtering to suggest relevant articles or services, while also incorporating content-based filtering to recommend based on user interests. Continuous feedback would help refine the recommendations over time.”

4. What considerations would you take into account when deploying a machine learning model in a production environment?

This question tests your understanding of the deployment process and operational challenges.

How to Answer

Mention aspects like scalability, monitoring, data privacy, and model retraining. Discuss the importance of ensuring the model performs well in real-world conditions.

Example

“When deploying a model, I would ensure it can handle the expected load and monitor its performance continuously. Data privacy is critical, especially in healthcare, so I’d implement strict access controls. Additionally, I’d set up a process for retraining the model with new data to maintain its accuracy.”

Cultural Fit

5. How do you approach collaboration with cross-functional teams?

This question assesses your ability to work within a team-oriented environment.

How to Answer

Discuss your communication style, how you handle differing opinions, and your approach to ensuring everyone is aligned on project goals.

Example

“I believe in open communication and regularly check in with team members to ensure alignment. When faced with differing opinions, I encourage discussions to understand various perspectives, which often leads to better solutions. I also make it a point to celebrate team successes to foster a collaborative spirit.”

6. Describe a time when you had to adapt to a significant change in a project.

This question evaluates your adaptability and resilience in a dynamic work environment.

How to Answer

Share a specific example where you faced a change, how you responded, and what the outcome was. Highlight your problem-solving skills.

Example

“During a project, we received feedback that the initial model was not meeting user needs. I quickly pivoted to gather more user input and adjusted our approach, incorporating additional features that aligned better with user expectations. This led to a more successful model and improved user satisfaction.”