Johnson & Johnson Machine Learning Engineer Interview Questions + Guide in 2025

Johnson & Johnson is a global leader in pharmaceuticals, medical devices, and consumer health products, dedicated to improving the health and well-being of people around the world.

As a Machine Learning Engineer at Johnson & Johnson, you'll play a crucial role in driving advancements in healthcare technology through the development and implementation of AI and ML solutions. Your primary responsibilities will include building end-to-end data science products that integrate seamlessly into the healthcare ecosystem, shaping and managing AI and ML initiatives, and collaborating closely with cross-functional teams to ensure successful delivery. You'll be expected to leverage your expertise in programming languages such as Python or R, along with machine learning frameworks like TensorFlow and PyTorch, to create scalable machine learning pipelines and operationalize data science solutions. This role requires strong problem-solving skills, a deep understanding of software development lifecycles, and the ability to communicate effectively with both technical and business stakeholders.

By preparing with this guide, you will be able to showcase your technical abilities, align your experiences with the company's values, and demonstrate your passion for innovation in healthcare during the interview process.

The interview process for a Machine Learning Engineer at Johnson & Johnson is structured and thorough, designed to assess both technical and behavioral competencies. The process typically unfolds over several weeks and consists of multiple stages, ensuring a comprehensive evaluation of candidates.

1. Initial Screening

The first step in the interview process is an initial screening, which usually takes place via a phone call or video interview with a recruiter. This conversation focuses on your background, experience, and motivation for applying to Johnson & Johnson. Expect to answer introductory questions about your resume and discuss your interest in the role and the company’s mission.

2. Technical Assessment

Following the initial screening, candidates often undergo a technical assessment. This may include a coding challenge or a live coding session where you will be asked to solve problems related to machine learning and data engineering. You might be evaluated on your proficiency in programming languages such as Python or R, as well as your understanding of machine learning frameworks like TensorFlow or PyTorch. Additionally, expect questions that assess your knowledge of algorithms, data manipulation, and model deployment.

3. Behavioral Interviews

Candidates typically participate in one or more behavioral interviews, which focus on assessing cultural fit and soft skills. Interviewers will likely use the STAR (Situation, Task, Action, Result) method to gauge how you handle challenges, work in teams, and align with the company’s values. Be prepared to discuss past experiences, particularly those that demonstrate your problem-solving abilities and teamwork.

4. Final Interviews

The final stage usually involves interviews with senior leadership or team members. This may include discussions about your technical projects, your approach to machine learning solutions, and how you would contribute to the team. You may also be asked to present a project or case study that showcases your skills and thought process. This stage is crucial for assessing your fit within the team and your ability to communicate complex ideas effectively.

5. Offer and Negotiation

If you successfully navigate the interview stages, you may receive a job offer. This will typically be followed by a discussion regarding salary and benefits. Be prepared to negotiate based on your experience and the industry standards.

As you prepare for your interview, it’s essential to familiarize yourself with the types of questions that may be asked during the process.

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at Johnson & Johnson. The interview process will likely assess both technical skills and behavioral competencies, so it's essential to prepare for a range of questions that cover your experience, problem-solving abilities, and understanding of machine learning concepts.

Technical Skills

1. Can you describe your experience with building end-to-end machine learning pipelines?

This question aims to evaluate your practical experience in developing machine learning solutions from conception to deployment.

How to Answer

Discuss specific projects where you built machine learning pipelines, detailing the tools and frameworks you used, as well as the challenges you faced and how you overcame them.

Example

“In my previous role, I developed an end-to-end machine learning pipeline for predicting patient outcomes. I utilized Python and TensorFlow for model development, and Docker for containerization. The project involved data preprocessing, feature engineering, model training, and deployment on AWS SageMaker, which significantly improved our prediction accuracy.”

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

This question assesses your knowledge of various machine learning algorithms and their applications.

How to Answer

Mention specific models you have worked with, explain their strengths and weaknesses, and provide examples of when you would choose one model over another based on the problem at hand.

Example

“I am well-versed in models such as logistic regression, random forests, and neural networks. For binary classification tasks, I often use logistic regression due to its interpretability. However, for more complex datasets with non-linear relationships, I prefer random forests or neural networks, as they tend to yield better performance.”

3. How do you approach feature engineering for a machine learning model?

This question evaluates your understanding of one of the most critical aspects of machine learning.

How to Answer

Explain your process for selecting, creating, and transforming features, and provide examples of how effective feature engineering has improved your model's performance.

Example

“I start by analyzing the dataset to identify potential features that could influence the target variable. I then create new features through transformations, such as log transformations for skewed data or interaction terms for capturing relationships. In a recent project, feature engineering led to a 20% increase in model accuracy.”

4. Can you explain the concept of overfitting and how to prevent it?

This question tests your understanding of model performance and generalization.

How to Answer

Define overfitting and discuss techniques you use to prevent it, such as cross-validation, regularization, or using simpler models.

Example

“Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, leading to poor performance on unseen data. To prevent this, I use techniques like cross-validation to ensure the model generalizes well, and I apply regularization methods like L1 or L2 to penalize overly complex models.”

Behavioral Questions

5. Describe a time when you faced a significant challenge in a project and how you overcame it.

This question assesses your problem-solving skills and resilience.

How to Answer

Use the STAR method (Situation, Task, Action, Result) to structure your response, focusing on the challenge, your approach, and the outcome.

Example

“In a previous project, we faced a significant data quality issue that threatened our timeline. I organized a team meeting to identify the root causes and developed a data cleaning strategy. By reallocating resources and prioritizing data quality, we not only met our deadline but also improved the model's accuracy by 15%.”

6. How do you prioritize tasks when working on multiple projects?

This question evaluates your time management and organizational skills.

How to Answer

Discuss your approach to prioritization, including any frameworks or tools you use to manage your workload effectively.

Example

“I prioritize tasks based on their impact and urgency. I use a project management tool to track deadlines and progress. For instance, when working on multiple machine learning projects, I assess which tasks align with business goals and allocate my time accordingly, ensuring that high-impact projects receive the attention they need.”

7. Why do you want to work for Johnson & Johnson?

This question gauges your motivation and alignment with the company’s values.

How to Answer

Express your interest in the company’s mission and how your skills and values align with their goals.

Example

“I admire Johnson & Johnson’s commitment to improving health outcomes and its focus on innovation in medicine. I believe my experience in developing machine learning solutions can contribute to your mission of transforming healthcare, and I am excited about the opportunity to work in such a collaborative and impactful environment.”

8. How do you stay current with advancements in machine learning and AI?

This question assesses your commitment to continuous learning and professional development.

How to Answer

Mention specific resources, such as online courses, conferences, or publications, that you engage with to stay updated.

Example

“I regularly read research papers from arXiv and attend conferences like NeurIPS and ICML. Additionally, I take online courses on platforms like Coursera to learn about the latest advancements in machine learning techniques and tools.”