AIML Software Engineer, AI for Science

About the role

GSK is strengthening its AI for Science (AI4S) team and is looking for Software Engineers with a track record of producing production-grade, data-driven software. You will design, build and operate the scalable cloud infrastructure and services that run the AI systems and agentic applications, including the serving of models, and keep them reliable in production. The role is hands-on software and platform engineering, supported by a team culture of ownership, accountability, continuous development and collaboration.

Key responsibilities

• Design, build and operate scalable infrastructure and services supporting AI models and agentic systems across the software development life cycle.
• Own reliability: set up CI/CD and release processes, automated testing, monitoring and alerting, and lead incident response.
• Build and operate model-serving infrastructure that exposes models in production with efficient use of compute.
• Develop and maintain cloud-native architectures for reliable deployment and scaling of AI/ML workloads.
• Deliver robust, tested, high-performance code in an agile environment, working closely with ML engineers and domain experts.

Required profile

• Degree in a quantitative or engineering discipline (e.g. computer science, computational biology, bioinformatics, engineering) or equivalent experience as a professional software engineer.
• Advanced Python programming and experience with cloud platforms (GCP, Azure) and cloud-native architectures.
• Basic understanding of at least one major deep learning framework (PyTorch, JAX, TensorFlow).
• Knowledge of command-line tools, shell scripting, CI/CD, containerization and infrastructure as code.
• Experience operating production services, administering SQL databases, and fluency in English.

Preferred qualifications

• Familiarity with machine learning principles and commercial cloud-native software.
• Experience deploying large-scale AI models and distributed training pipelines, and with SLOs/SLIs.
• Infrastructure-as-code (e.g. Terraform), Kubernetes (e.g. GKE), and GCP networking and security controls.
• Open-source contributions, knowledge of disease biology and biomedical data.

The position is listed as hybrid (#LI-Hybrid). Applications are made through the online application form, using the cover letter to describe how you meet the competencies.