AIML Software Engineer, AI for Science

At GSK, we are actively working on building a future in which state-of-the-art software, Artificial Intelligence (AI) and Machine Learning (ML) enable us to develop new therapies and personalized medicines that drive better outcomes for patients at reduced cost with fewer side effects. This ambitious mission requires scalable, cloud-native solutions at the forefront of Software Engineering, Cloud Infrastructure, Efficient Compute, Machine Learning and AI. If this excites you, we would love to chat.

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 design, build and operate the scalable cloud infrastructure and services – including the serving of our models – that our AI systems and agentic applications run on, and you will be accountable for keeping them reliable in production. This is hands-on software and platform engineering: building robust, well-tested, high-performance systems that scientists across GSK depend on every day, on modern cloud technology and the vast biomedical data sources available to us.

Team Culture

The AI4S team is built on the principles of ownership, accountability, continuous development, and collaboration. We hire for the long term, and we are motivated to make this a great place to work. Our leaders will be committed to your career and development from day one.

We strongly encourage applications from people with diverse and underrepresented backgrounds and perspectives.

In this role you will

• Design, build and operate scalable infrastructure and services that support our AI models and agentic systems across the entire software development life cycle.
• Own the reliability of what you build – set up CI/CD and release processes, automated testing, monitoring and alerting, and lead the response when things break, so the systems scientists rely on stay dependable.
• Build and operate the model-serving infrastructure that exposes our models in production with efficient use of compute.
• Develop and maintain cloud-native architectures that enable reliable deployment and scaling of AI/ML workloads.
• Deliver robust, tested and high-performance code in an agile environment, and work closely with ML engineers and domain experts to make the infrastructure fit for purpose.

Why you?

Qualifications & Skills

• 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 using frameworks like FastAPI.
• Experience with cloud platforms (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.
• Experience operating production services – monitoring, observability and alerting, and diagnosing and resolving issues in live systems.
• Experience designing and administering SQL databases – schema design, query performance, and day-to-day operational management.
• Hands-on experience with Google Cloud Platform, in particular the services we build on: Cloud Run, Google Kubernetes Engine, Cloud Storage, Artifact Registry, Cloud SQL.
• Fluency in English.

Preferred Qualifications & Skills

• 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.
• Experience running production services at scale, including defining and working to service-level objectives (SLOs/SLIs).
• Experience with incident response and post-incident review, and with building the observability that supports it.
• Infrastructure-as-code (e.g. Terraform) for provisioning and maintaining cloud environments.
• Experience developing and administering workloads on Kubernetes (e.g. GKE).

#J-18808-Ljbffr