GSK Machine Learning Engineer Interview Questions + Guide in 2025

GSK is a global healthcare company dedicated to uniting science, technology, and talent in the pursuit of advancing health and getting ahead of disease.

As a Machine Learning Engineer at GSK, you will play a pivotal role in designing and implementing machine learning models aimed at improving healthcare outcomes. You will be responsible for developing and deploying machine learning-powered tools, collaborating closely with cross-functional teams to translate complex biomedical data into actionable insights. A strong foundation in software engineering, particularly in Python and C++, is essential, along with expertise in deep learning frameworks such as PyTorch or TensorFlow. The ideal candidate will possess a solid understanding of modern ML architectures and practices, with a background in the biomedical sciences, particularly in genetics or molecular biology.

In this role, you will be expected to approach poorly defined problems with analytical rigor, establish clear objectives, and execute them into high-performing solutions. Your contributions will also involve mentoring junior team members and setting high standards for code quality and best practices. GSK values innovative thinking and a commitment to continuous improvement, making it critical for you to embody a mindset of “commit early and often” while maintaining a focus on delivering high-quality production code.

This guide will equip you with valuable insights into the role and expectations, helping you prepare effectively for your interview at GSK.

The interview process for a Machine Learning Engineer at GSK is structured and involves multiple stages designed to assess both technical and behavioral competencies.

1. Initial Screening

The process typically begins with an initial screening, which may be conducted via a phone call with a recruiter. This conversation focuses on your background, motivation for applying, and an overview of the role. The recruiter will also gauge your fit within GSK’s culture and values.

2. Coding Assessment

Following the initial screening, candidates are usually required to complete a coding assessment. This is often conducted on platforms like HackerRank and includes LeetCode-style questions that test your programming skills, particularly in Python or C++. The coding test is known to have a competitive passing rate, so preparation is key.

3. Technical Interviews

Candidates can expect to participate in multiple technical interviews, typically three rounds, each focusing on different areas of expertise. These interviews may cover topics such as probability and statistics, applied machine learning, deep learning frameworks (like PyTorch or TensorFlow), and general software engineering principles. Interviewers will assess your understanding of machine learning concepts and your ability to apply them to real-world problems.

4. Behavioral Interviews

In addition to technical assessments, behavioral interviews are a significant part of the process. These interviews often utilize the STAR (Situation, Task, Action, Result) method to evaluate how you have handled past experiences and challenges. Interviewers will be interested in your teamwork, problem-solving skills, and how you align with GSK’s mission and values.

5. Final Interview and Presentation

The final stage may involve a more in-depth interview, which could include a presentation of a relevant project or a case study. This round often includes a panel of interviewers who will ask both technical and behavioral questions to further assess your fit for the role and the team.

Despite the structured nature of the process, candidates have reported instances of lack of communication post-interview, so it’s advisable to follow up if you do not hear back within a reasonable timeframe.

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 Interview Process

GSK’s interview process typically involves multiple stages, including initial screenings, coding tests, and technical interviews. Familiarize yourself with the structure of these interviews, as candidates have reported a mix of behavioral and technical questions. Be prepared for a coding test on platforms like HackerRank, which may include LeetCode-style questions. Knowing the format will help you manage your time and expectations effectively.

Prepare for Technical Depth

As a Machine Learning Engineer, you should be well-versed in various machine learning frameworks such as PyTorch and TensorFlow. Candidates have noted that interviews often include questions on probability, statistics, and applied machine learning. Brush up on your knowledge of modern ML architectures and be ready to discuss your experience with deploying ML services. Additionally, be prepared to solve real-world problems, as interviewers may present scenarios to assess your problem-solving skills.

Showcase Your Projects

Candidates have found that discussing personal projects, especially those related to machine learning, can set you apart. Be ready to explain your contributions to these projects, the challenges you faced, and how you overcame them. Highlight any open-source contributions, as GSK values a mentality of collaboration and sharing knowledge within the community.

Emphasize Behavioral Competencies

GSK places importance on cultural fit, so expect behavioral questions that assess your motivation and past experiences. Use the STAR method (Situation, Task, Action, Result) to structure your responses. Prepare examples that demonstrate your ability to work in teams, mentor others, and maintain high standards in code quality and best practices.

Be Proactive in Communication

While candidates have reported instances of being ghosted after interviews, it’s essential to maintain professionalism. After your interviews, follow up with a thank-you email to express your appreciation for the opportunity. If you don’t hear back within a reasonable timeframe, consider sending a polite follow-up to inquire about your application status. This shows your continued interest and can help you stand out.

Align with Company Values

GSK is focused on uniting science, technology, and talent to advance healthcare. Familiarize yourself with their mission and values, and be prepared to discuss how your skills and experiences align with their goals. Demonstrating a genuine interest in their work and how you can contribute will resonate well with interviewers.

By following these tips, you can approach your interview with confidence and a clear strategy, increasing your chances of success at GSK. Good luck!

In this section, we’ll review the various interview questions that might be asked during a Machine Learning Engineer interview at GSK. The interview process will likely assess your technical skills in machine learning, software engineering, and your ability to apply these skills in a biomedical context. Be prepared to discuss your past experiences, technical knowledge, and problem-solving abilities.

Technical Skills

1. Describe a machine learning project you have worked on from start to finish. What were the challenges, and how did you overcome them?

This question aims to evaluate your hands-on experience and problem-solving skills in machine learning projects.

How to Answer

Discuss the project scope, your role, the challenges faced, and the solutions you implemented. Highlight any specific techniques or tools you used.

Example

“I worked on a project to develop a predictive model for patient outcomes using electronic health records. One major challenge was dealing with missing data, which I addressed by implementing imputation techniques. The project not only improved prediction accuracy but also provided actionable insights for clinical decision-making.”

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 algorithms used in each category.

Example

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

3. What are variational autoencoders, and how do they differ from traditional autoencoders?

This question assesses your understanding of advanced machine learning techniques.

How to Answer

Explain the concept of variational autoencoders and their applications, emphasizing their probabilistic nature compared to traditional autoencoders.

Example

“Variational autoencoders (VAEs) are generative models that learn to encode input data into a latent space while also capturing the distribution of the data. Unlike traditional autoencoders, which focus on reconstruction, VAEs introduce a probabilistic approach, allowing for the generation of new data points that resemble the training data.”

4. How do you handle overfitting in machine learning models?

This question evaluates your understanding of model evaluation and improvement techniques.

How to Answer

Discuss various strategies to mitigate overfitting, such as regularization, cross-validation, and using simpler models.

Example

“To combat overfitting, I often use techniques like L1 and L2 regularization to penalize complex models. Additionally, I implement cross-validation to ensure that the model generalizes well to unseen data, and I may also simplify the model architecture if necessary.”

5. Describe your experience with deep learning frameworks like TensorFlow or PyTorch.

This question gauges your practical experience with popular machine learning tools.

How to Answer

Share specific projects or tasks where you utilized these frameworks, highlighting your proficiency and any challenges faced.

Example

“I have extensive experience using PyTorch for developing deep learning models, particularly in image classification tasks. I appreciate its dynamic computation graph, which allows for easier debugging and experimentation. In one project, I implemented a convolutional neural network that achieved a 95% accuracy rate on a medical imaging dataset.”

Software Engineering

1. How do you ensure code quality and maintainability in your projects?

This question assesses your software engineering practices.

How to Answer

Discuss your approach to writing clean code, using version control, and conducting code reviews.

Example

“I prioritize code quality by adhering to best practices such as writing modular code, using meaningful variable names, and including comments. I also utilize version control systems like Git for collaboration and conduct regular code reviews with my team to ensure maintainability.”

2. Can you explain the importance of version control in collaborative projects?

This question evaluates your understanding of collaborative software development.

How to Answer

Highlight the benefits of version control systems in managing code changes and collaboration among team members.

Example

“Version control is crucial in collaborative projects as it allows multiple developers to work on the same codebase without conflicts. It provides a history of changes, enabling easy tracking of modifications and facilitating rollbacks if necessary. This ensures that the team can collaborate efficiently and maintain a stable codebase.”

3. Describe a time when you had to debug a complex issue in your code. What was your approach?

This question tests your problem-solving and debugging skills.

How to Answer

Outline the steps you took to identify and resolve the issue, emphasizing your analytical skills.

Example

“I encountered a complex bug in a machine learning pipeline that caused inconsistent model performance. I started by isolating components of the pipeline to identify where the issue originated. After thorough testing, I discovered that a data preprocessing step was introducing noise. I revised the preprocessing logic, which resolved the issue and improved model accuracy.”

4. What strategies do you use for testing machine learning models?

This question assesses your understanding of model validation and testing methodologies.

How to Answer

Discuss various testing strategies, including cross-validation, A/B testing, and performance metrics.

Example

“I employ cross-validation to assess model performance on different subsets of data, ensuring that the model generalizes well. Additionally, I use A/B testing in production to compare the performance of different models in real-time, allowing for data-driven decisions on which model to deploy.”

5. How do you approach the deployment of machine learning models?

This question evaluates your understanding of the deployment process and best practices.

How to Answer

Explain your experience with deploying models, including considerations for scalability and monitoring.

Example

“When deploying machine learning models, I focus on creating a robust pipeline that includes continuous integration and delivery practices. I also implement monitoring tools to track model performance in production, allowing for timely updates and adjustments based on real-world data.”