AIML Engineer, AI for Science

Role Summary

To strengthen our AI for Science (AI4S) team, we are looking for AI/ML Engineers with a track record in developing and validating machine learning models for real-world scientific problems. You will drive 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.

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.

Qualifications

• Required: 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.
• Required: Experience with at least one major deep learning framework (PyTorch, JAX, TensorFlow).
• Required: Familiarity with machine learning literature and state-of-the-art approaches.
• Required: Experience developing and delivering robust software solutions, including demonstrated advanced programming expertise in Python.
• Required: Experience in software engineering and machine learning best practices, including version control, continuous integration (CI) and continuous development (CD), containerization, and shell scripting.
• Required: Fluency in English.

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