Interview Guides

GSK Machine Learning Engineer Interview Questions 2026

GSKMachine Learning Engineer

Updated Jun 10, 2026

GSK Machine Learning Engineer interview questions & guide 2026

Every question GSK interviewers actually ask, the frameworks that win the room, and the language hiring managers respond to.

Question bank

What is a Machine Learning Engineer at GSK?

A Machine Learning Engineer at GSK plays a pivotal role in bridging the gap between advanced artificial intelligence research and practical, life-saving healthcare applications. At GSK, machine learning is not just an optimization tool; it is a core driver of modern drug discovery, vaccine development, and genomic analysis. By designing, building, and scaling robust machine learning models, you directly contribute to reducing the time and cost required to bring critical medicines to patients globally.

In this role, you will work alongside computational biologists, chemists, data engineers, and clinical researchers to transform complex biological data into actionable insights. This involves handling massive, high-dimensional datasets, including genomic sequences, chemical structures, and clinical trial results. Your primary focus will be on developing scalable training pipelines, optimizing deep learning architectures, and ensuring that models are successfully deployed and integrated into production-level scientific workflows.

The work is highly interdisciplinary and technically demanding, requiring a deep understanding of both software engineering best practices and cutting-edge machine learning theory. Succeeding as a Machine Learning Engineer at GSK means writing clean, reproducible code that can run efficiently at scale, while remaining curious about the complex scientific domains your models are helping to decode.

Common Interview Questions

The following questions are representative of what you can expect during the GSK hiring process. These questions are drawn from real candidate experiences and are designed to test your coding fluency, theoretical understanding, and ability to apply machine learning to real-world datasets.

Coding & Algorithmic Problem Solving

These questions evaluate your core programming skills in Python, your familiarity with scientific computing libraries, and your ability to write clean, efficient code under time constraints.

Write a Python function to preprocess a raw biological dataset, handling missing values and normalizing feature scales without using external libraries.
Implement a custom PyTorch dataset class that reads, batches, and tokenizes sequence data for a deep learning model.

Given an array of integers, write an efficient algorithm to find the longest contiguous subarray with a sum equal to a target value.
Explain how you would optimize a slow-running Python script that processes large text files containing clinical trial notes.
Write a function to perform matrix multiplication from scratch, and explain how you would optimize its computational complexity.

Machine Learning Theory & Neural Networks

These questions assess your foundational understanding of machine learning algorithms, deep learning architectures, and the mathematical principles that govern them.

Explain the mathematical difference between L1 and L2 regularization, and describe scenarios where you would prefer one over the other.
Walk me through the mechanics of backpropagation in a multi-layer perceptron. How do vanishing and exploding gradients occur, and how do you mitigate them?
What are the architectural differences between a Convolutional Neural Network (CNN) and a Transformer model when processing sequential data?
Describe how you would evaluate a binary classification model trained on a highly imbalanced medical dataset. Which metrics would you prioritize?
Explain the concept of self-supervised learning and how it can be applied to unlabeled chemical structure data.

Technical Background & System Design

These questions focus on your past engineering experience, your ability to design scalable ML systems, and how you approach model deployment and monitoring.

Walk me through a complex machine learning project you led from conception to production. What were the main technical bottlenecks, and how did you resolve them?
How would you design a scalable pipeline to continuously train and redeploy models as new clinical data becomes available?
Describe your experience with containerization (e.g., Docker) and orchestration (e.g., Kubernetes) in the context of deploying ML workloads.
How do you ensure reproducibility in your machine learning experiments when working across distributed teams?
Tell me about a time you had to optimize a model's inference latency for a real-time production application. What trade-offs did you make?

See every interview question for this role

03 · Question bank

The questions most likely to come up

Sorted by reported frequency

#QuestionTopicDifficultyAsked

01Prioritizing Across Competing Client ProjectsBehavioral & LeadershipEasyVery common

02Leading Through an Ambiguous Project CrisisBehavioral & LeadershipMediumVery common

03Explain Precision vs RecallModel EvaluationEasyVery common

04Handling Missing Values in MLMachine LearningEasyVery common

05Prevent Overfitting in ML ModelsMachine LearningEasyVery common

06Design Feature Drift Monitoring SystemML System DesignHardVery common

07Operationalize Model Deployment PipelinePipelinesEasyVery common

08Interpret F1 for Imbalanced ClassificationModel EvaluationEasyVery common

09Choose Precision vs Recall BiasModel EvaluationMediumVery common

10Tune Hyperparameters for Model SelectionMachine LearningMediumVery common

11Handle Missing Sensor DataMachine LearningEasyVery common

12Evaluate F1 Score Significance in Model PerformanceModel EvaluationMediumVery common

13Evaluate Cross-Validation Impact on Model PerformanceModel EvaluationMediumVery common

14Reducing Overfitting in ML ModelsMachine LearningMediumVery common

15Handling Overfitting in Predictive ModelsMachine LearningMediumVery common

16Evaluate Model Metrics for Customer Churn PredictionModel EvaluationMediumVery common

17Troubleshoot Underperforming Support ClassifierModel EvaluationEasyVery common

18Predict Loan Default for FintechMachine LearningEasyVery common

19Prevent Overfitting in Loan Default ModelsMachine LearningEasyCommon

20Detect Leakage in Feature EngineeringModel EvaluationMediumSometimes

Unlock every question, framework, and sample answer

04 · Sample answer

See how a strong candidate would approach this

EasyAsked 4239+ times

Prioritizing Across Competing Client Projects

Why they ask: Tests structured thinking and the candidate's ability to navigate ambiguity. Interviewers want a clear framework over a heroic answer.

Practice this

The framework for this question is on the practice page.

Getting Ready for Your Interviews

Preparing for the GSK interview process requires a balanced approach that addresses both your software engineering capabilities and your theoretical machine learning expertise. You should approach your preparation with a structured mindset, focusing on demonstrating clear communication and technical depth.

Technical Rigor – You must show that you can write clean, production-ready Python code. This means focusing on code efficiency, modular design, and a deep familiarity with core libraries like PyTorch, NumPy, and Pandas.

Scientific Curiosity – While a background in biology or chemistry is not always mandatory, you must show a strong interest in applying your technical skills to healthcare challenges. Be prepared to discuss how your engineering choices impact downstream scientific outcomes.

Systemic Problem-Solving – Interviewers at GSK want to see how you approach open-ended, ambiguous problems. When designing pipelines or architectures, always explain the trade-offs you are making regarding scalability, latency, and model interpretability.

Interview Process Overview

The interview process for a GSK Machine Learning Engineer typically takes between three to six weeks to complete, depending on the location and specific team. The process is designed to evaluate your practical coding skills early on, followed by deep dives into your theoretical knowledge and past engineering achievements.

You will typically begin with an automated technical assessment, which is highly rigorous and serves as the primary filter for the technical rounds. If you pass this initial stage, you will progress through a series of structured conversations with the hiring manager and specialized technical panels.

Tip

The automated coding assessment is a critical hurdle in the GSK process. Ensure you allocate uninterrupted time and practice core PyTorch and data preprocessing tasks beforehand to feel confident under the strict time limits.

The final stages focus heavily on your ability to collaborate, your understanding of deep learning architectures, and your alignment with GSK's mission to deliver impactful healthcare solutions.

07 · The loop

The interview process, end to end

≈ 3-5 weeks · 3 rounds

Automated Technical Assessment

A rigorous coding assessment that serves as the primary filter for the technical rounds.

Structured Conversations

Series of discussions with the hiring manager and specialized technical panels.

Final Stages

Focus on collaboration skills, understanding of deep learning architectures, and alignment with GSK's mission.

This visual timeline illustrates the typical progression of stages you will navigate during the hiring process. Use this overview to structure your weekly preparation, ensuring you master coding fundamentals before moving on to complex neural network theory and system design.

Deep Dive into Evaluation Areas

To succeed in the GSK technical rounds, you must understand exactly what the interviewers are evaluating in each core area. The technical assessment is designed to test your practical engineering skills, your conceptual depth, and your ability to articulate your design choices.

Algorithmic Coding & PyTorch Implementation

This area evaluates your hands-on coding speed, accuracy, and engineering discipline. You will be expected to manipulate data structures, write efficient algorithms, and build custom deep learning components using PyTorch.

Be ready to go over:

Tensor Operations – Efficiently manipulating multi-dimensional arrays, understanding broadcasting, and avoiding common memory bottlenecks in PyTorch.
Data Preprocessing Pipelines – Writing robust code to clean, normalize, and transform raw, noisy datasets into model-ready formats.
Custom Model Components – Implementing custom loss functions, layers, or training loops from scratch without relying on high-level wrappers.
Advanced concepts (less common) – Distributed training strategies, writing custom CUDA extensions, and optimizing memory usage during large-scale training.

Example questions or scenarios:

"Implement a custom PyTorch layer that performs a specialized spatial transformation on input feature maps."
"Write a pipeline to load, clean, and batch a dataset of genetic sequences, ensuring that variable-length sequences are padded efficiently."

Deep Learning & Neural Network Fundamentals

This evaluation area goes beyond coding to test your conceptual and mathematical understanding of deep neural networks. Interviewers want to ensure you understand why models behave the way they do, rather than just knowing how to import them from libraries.

Be ready to go over:

Optimization Algorithms – The mathematical differences between Adam, SGD, and RMSprop, and how learning rate schedules affect convergence.
Regularization Techniques – Dropout, batch normalization, weight decay, and their specific impacts on generalization and training dynamics.
Architecture Selection – Choosing the right model architecture (e.g., LSTMs, Transformers, GNNs) based on the structural properties of your input data.
Advanced concepts (less common) – Graph Neural Networks (GNNs) for molecular data, self-supervised pre-training strategies, and neural architecture search.

Example questions or scenarios:

"Explain the vanishing gradient problem in deep networks. How do residual connections mathematically resolve this issue?"
"How would you design a neural network to predict the binding affinity of a small molecule to a target protein? What data representation would you use?"

Note

During live coding rounds, you may be asked to write code on a shared screen without access to search engines or documentation. Practice writing core Python and PyTorch code offline to build confidence and fluency.

Technical Background & Resume Integrity

Your past experiences will be scrutinized to assess your practical engineering maturity. Interviewers will ask highly specific questions about the projects on your CV to verify your contributions and understand your decision-making processes.

Be ready to go over:

Architectural Trade-offs – Explaining why you chose a specific model, database, or pipeline structure over viable alternatives.
Production Challenges – Discussing real-world failures, model drift, data quality issues, and how you resolved them in previous roles.
Scientific Collaboration – How you translated complex technical ML concepts to non-technical stakeholders or domain experts like biologists and clinicians.

Example questions or scenarios:

"In your previous role, you mentioned deploying a model that reduced inference time by 40%. Walk me through the exact optimization steps you took."
"What did you find most challenging about your Master's thesis or previous research project, and how did you validate your final results?"

09 · Topic breakdown

What they actually test for

Topic distribution

All topics

PythonMachine Learning TheoryNeural Networks FundamentalsPyTorchData Preprocessing

Key Responsibilities

As a Machine Learning Engineer at GSK, your day-to-day responsibilities will span the entire machine learning lifecycle, from initial research exploration to production deployment.

You will design, implement, and maintain scalable data pipelines that ingest and process massive biological, chemical, and clinical datasets. This requires writing highly optimized ETL processes that can handle high-dimensional, noisy data while ensuring strict data governance and quality standards.

You will also collaborate closely with research scientists to translate complex biological hypotheses into concrete machine learning problems. This involves training, tuning, and validating state-of-the-art models, ranging from traditional random forests to advanced graph neural networks and large language models.

Once a model is validated, you will be responsible for containerizing and deploying it to cloud environments, ensuring that inference pipelines are highly available, secure, and computationally efficient. You will also build robust monitoring systems to track model performance, detect data drift, and orchestrate automated retraining pipelines.

Role Requirements & Qualifications

To be competitive for this role at GSK, you must demonstrate a strong blend of software engineering discipline and machine learning expertise.

Must-have technical skills – Advanced proficiency in Python and deep learning frameworks, specifically PyTorch. You must have solid experience building data preprocessing pipelines and a strong grasp of software engineering fundamentals (version control, CI/CD, unit testing).
Nice-to-have technical skills – Experience with cloud platforms (AWS, Azure, or GCP), containerization tools (Docker, Kubernetes), and big data technologies (Spark, SQL). Familiarity with bioinformatics, cheminformatics, or processing genomic data is highly advantageous.
Experience level – Typically, a minimum of two to five years of professional experience as an ML engineer, software engineer, or data scientist in a production environment. An advanced degree (MSc or PhD) in Computer Science, Machine Learning, Bioinformatics, or a related quantitative field is highly preferred.
Soft skills – Strong communication skills, with a proven ability to explain complex technical concepts to non-technical stakeholders. You must be highly collaborative, comfortable working in cross-functional teams, and possess a strong passion for applying technology to solve complex healthcare and scientific challenges.

Frequently Asked Questions

Q: How difficult is the GSK Machine Learning Engineer interview process? A: The process is generally rated as average to challenging. The primary difficulty lies in the length and rigor of the initial HackerRank assessment, which can range from 90 minutes to 3 hours, and the requirement to code without internet assistance during live technical rounds.

Q: How long does the entire hiring process take? A: On average, the process takes about a month from your initial application to a final decision. However, some candidates have experienced delays or administrative gaps between rounds, so maintaining active communication with your recruiter is highly recommended.

Q: What is the hybrid or remote work policy for ML Engineers at GSK? A: GSK generally operates on a hybrid model, requiring employees to be in the office (such as the London or Cambridge hubs) a few days a week to foster collaboration with cross-functional scientific teams. Specific arrangements should be clarified with your recruiter early in the process.

Q: How much domain-specific biological knowledge do I need? A: While prior experience in pharma, biotech, or bioinformatics is highly valued, it is not an absolute prerequisite. GSK values strong foundational software engineering and machine learning skills; they are fully prepared to help you build the necessary domain knowledge on the job.

Q: Can I use AI assistants or LLMs during the online technical tests? A: This can vary by team and assessment platform. While some recruiters may state that LLMs are permitted for reference, the testing platforms themselves (like HackerRank) often have strict anti-plagiarism and AI-detection protocols. It is highly recommended to complete all assessments without AI assistance to avoid automatic disqualification.

Other General Tips

To maximize your chances of success during the GSK interview process, keep these practical, insider tips in mind:

Master Offline Coding: Practice writing clean, syntactically correct Python and PyTorch code without the aid of autocomplete, Stack Overflow, or search engines. This will prepare you for the live, screen-shared coding rounds.
Clarify Assessment Rules: Before starting your online HackerRank test, obtain clear, written confirmation from your recruiter regarding the specific policy on using external resources or LLMs to avoid any platform-level flags.
Know Your CV Inside Out: Be ready to defend every bullet point on your resume. GSK hiring managers often ask highly specific, deep-dive questions about your previous academic and professional projects to verify your technical depth.
Bridge Tech and Science: When discussing your past work, emphasize not just the algorithms you used, but the real-world impact of your models. Explain how your technical choices solved a concrete business or scientific problem.

Tip

Be patient and proactive with recruiter communication. If you do not hear back within a week of completing a stage, send a polite follow-up email to keep your application moving forward through the pipeline.

Summary & Next Steps

The Machine Learning Engineer position at GSK offers an extraordinary opportunity to apply cutting-edge artificial intelligence to some of the world's most challenging healthcare problems. From accelerating drug discovery to optimizing clinical trials, your work will have a tangible, positive impact on global human health.

To succeed in this highly competitive process, focus your preparation on mastering Python and PyTorch fundamentals, refining your understanding of deep learning theory, and practicing your live coding skills. Approach each round with a collaborative mindset and a genuine curiosity for the scientific domains that GSK operates in.

The salary data reflects the competitive compensation packages offered to technical talent in this sector. When reviewing these figures, consider the full compensation structure, which typically includes a base salary, performance-related bonuses, and comprehensive health and wellness benefits aligned with GSK's commitment to employee well-being.

To further refine your preparation, explore additional real-world interview insights, detailed company reviews, and extensive practice resources on Dataford. With focused preparation, deep technical practice, and a clear understanding of GSK's mission, you are well-positioned to ace your interviews and secure this impactful role.

15 · More at this company

Other roles at GSK

Software Engineer Engineering Manager GenAI Engineer Product Manager Data Analyst QA Engineer

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GSKMachine Learning Engineer

Updated Jun 10, 2026

GSK Machine Learning Engineer interview questions & guide 2026

Every question GSK interviewers actually ask, the frameworks that win the room, and the language hiring managers respond to.

Question bank

What is a Machine Learning Engineer at GSK?

Common Interview Questions

Coding & Algorithmic Problem Solving

These questions evaluate your core programming skills in Python, your familiarity with scientific computing libraries, and your ability to write clean, efficient code under time constraints.

Write a Python function to preprocess a raw biological dataset, handling missing values and normalizing feature scales without using external libraries.
Implement a custom PyTorch dataset class that reads, batches, and tokenizes sequence data for a deep learning model.

Given an array of integers, write an efficient algorithm to find the longest contiguous subarray with a sum equal to a target value.
Explain how you would optimize a slow-running Python script that processes large text files containing clinical trial notes.
Write a function to perform matrix multiplication from scratch, and explain how you would optimize its computational complexity.

Machine Learning Theory & Neural Networks

These questions assess your foundational understanding of machine learning algorithms, deep learning architectures, and the mathematical principles that govern them.

Explain the mathematical difference between L1 and L2 regularization, and describe scenarios where you would prefer one over the other.
Walk me through the mechanics of backpropagation in a multi-layer perceptron. How do vanishing and exploding gradients occur, and how do you mitigate them?
What are the architectural differences between a Convolutional Neural Network (CNN) and a Transformer model when processing sequential data?
Describe how you would evaluate a binary classification model trained on a highly imbalanced medical dataset. Which metrics would you prioritize?
Explain the concept of self-supervised learning and how it can be applied to unlabeled chemical structure data.

Technical Background & System Design

These questions focus on your past engineering experience, your ability to design scalable ML systems, and how you approach model deployment and monitoring.

Walk me through a complex machine learning project you led from conception to production. What were the main technical bottlenecks, and how did you resolve them?
How would you design a scalable pipeline to continuously train and redeploy models as new clinical data becomes available?
Describe your experience with containerization (e.g., Docker) and orchestration (e.g., Kubernetes) in the context of deploying ML workloads.
How do you ensure reproducibility in your machine learning experiments when working across distributed teams?
Tell me about a time you had to optimize a model's inference latency for a real-time production application. What trade-offs did you make?