AIML Engineer, AI for Science

GSK

2d•Hybrid

About The Position

At GSK, we see a world where advanced applications of Machine Learning and AI enable us to develop transformational medicines that drive better outcomes for patients at reduced cost and with fewer side effects. AI will also play a role in how we diagnose and use medicines to enable everyone to do more, feel better, and live longer. It is an ambitious vision that will require the development of products at the cutting edge of Machine Learning and AI. To strengthen the AI for Science (AI4S) team, GSK is looking for AI/ML Engineers with a track record in developing and validating machine learning models for real-world scientific problems. This role involves driving the development of AI models and agentic systems — researching, designing, implementing, and delivering solutions across a range of scientific tasks, including open-ended research questions, leveraging high-performance computing and the vast biomedical data sources available at GSK. The AI4S team fosters a culture of ownership, accountability, continuous development, and collaboration, committed to long-term career growth and development.

Requirements

Bachelor’s, Master’s or Doctorate degree in a quantitative or engineering discipline (computer science, computational biology, bioinformatics, engineering, among others); OR equivalent work experience delivering state-of-the-art AI/ML solutions.
Experience with at least one major deep learning framework (PyTorch, JAX, TensorFlow).
Familiarity with machine learning literature and state-of-the-art approaches.
Experience developing and delivering robust software solutions, including demonstrated advanced programming expertise in Python.
Experience in software engineering and machine learning best practices, including version control, continuous integration (CI) and continuous development (CD), containerization, and shell scripting.
Fluency in English.

Nice To Haves

Experience in design, development and deployment of commercial AI/ML software.
Experience with Large Language Models (LLMs) and Agentic AI (e.g., tool use, multi-agent orchestration, deployment and evaluation).
Contributions to relevant open-source projects.
Relevant scientific publications in AI/ML (e.g., NeurIPS, ICML, ICLR, AAAI), computational biology or bioinformatics venues.
Knowledge of or interest in disease biology, molecular biology and medicine.
Experience working with biomedical data (e.g., genomics, transcriptomics, proteomics, electronic health records, clinical images).

Responsibilities

Design and implement AI/ML-driven solutions throughout the entire model development life cycle.
Research and develop state‑of‑the‑art machine learning models and agentic systems to solve a variety of scientific tasks.
Deliver robust, tested, and high-performance code in an agile environment.
Liaise with experts in biology, medicine and experimentation to ensure optimal data collection and processing for machine learning models.