AIML Software Engineer, AI for Science

About the Role

To strengthen our AI for Science (AI4S) team, we are looking for Software Engineers with a track record in developing production-grade, data-driven software solutions. You will drive the development of scalable cloud infrastructure and efficient compute solutions to support large-scale AI models and agentic systems — building robust, high-performance software that enables scientific research using modern cloud technologies and the vast biomedical data sources available at GSK.

Responsibilities

In this role you will

• Design and implement scalable infrastructure and software solutions to support large-scale AI models and agentic systems across the entire software development life cycle.
• Design and implement sophisticated machine learning and deep learning pipelines that can handle massive amounts of data with optimal resource utilization.
• Develop and maintain cloud-native architectures that enable seamless deployment and scaling of AI/ML workloads.
• Deliver robust, tested and high-performance code in an agile environment.
• Liaise with AI/ML engineers, data scientists, and domain experts to ensure fit-for-purpose infrastructure and data pipelines for cutting-edge scientific projects.

Qualifications

We are looking for professionals with these required skills to achieve our goals:

• A degree in a quantitative or engineering discipline (e.g., computer science, computational biology, bioinformatics, engineering, among others); OR equivalent work experience as a professional software engineer.
• Demonstrated advanced programming expertise in Python and in developing and delivering robust, scalable software solutions.
• Experience with cloud platforms (AWS, GCP, Azure) and cloud-native architectures.
• Passion for software design and commitment to the development of reusable, scalable, and testable software components.
• Basic understanding of at least one major deep learning framework (PyTorch, JAX, TensorFlow).
• Knowledge of command-line tools and shell scripting.
• Knowledge of software engineering best practices, including continuous integration (CI) and continuous deployment (CD), containerization, and infrastructure as code.
• Strong problem-solving and debugging skills, and experience working in cluster settings or cloud-based environments.
• Fluency in English.

Preferred Qualifications

If you have the following characteristics, it would be a plus:

• Familiarity with machine learning principles and state-of-the-art modelling approaches.
• Experience in design, development and deployment of commercial cloud-native software and infrastructure.
• Experience building and deploying large-scale AI models and agentic systems in production environments.
• Experience architecting, developing, and deploying distributed training pipelines for large models with PyTorch or TensorFlow.
• Expertise in performance optimization, cost optimization, and efficient compute resource management in cloud environments.
• Contributions to relevant open-source projects.
• Knowledge or interest in disease biology, molecular biology and medicine.
• Experience working with biomedical data (e.g., genomics, transcriptomics, proteomics, electronic health records, clinical images).

Why GSK?

Uniting science, technology and talent to get ahead of disease together.