Director, AI/ML Engineering – Clinical AI in Stanley

This job is with GSK, an inclusive employer. At GSK we see a world in which advanced applications of machine learning and AI will allow us to develop novel therapies to existing diseases and to quickly respond to emerging or changing diseases with personalized drugs, driving better outcomes at reduced cost with fewer side effects. It is an ambitious vision that will require the development of products and solutions at the cutting edge of machine learning and AI.

We are looking for a Director of AI/ML Engineering to lead a team focused on clinical AI. In this role you will lead a high-performing team of ML Engineers to design, develop, and deploy production‑grade AI/ML products that accelerate GSK’s drug development programs. You will be accountable for building end‑to‑end clinical AI/ML solutions—from problem framing and data strategy, through model development and validation, to deployment and lifecycle management in production environments that support clinical trials and regulatory decision‑making. The AI/ML team is built on the principles of ownership, accountability, continuous development, and collaboration. We hire for the long term, and we’re motivated to make this a great place to work. Our leaders will be committed to your career and development from day one.

Responsibilities

• Define an AI/ML strategy for clinical development, aligned with program and portfolio objectives.
• Identify high‑impact use cases across the clinical pipeline (e.g. patient segmentation using multimodal data, and predictive models for disease progression, treatment response, and clinical outcomes in priority diseases).
• Be a standard bearer for data science and software engineering best practices within the organisation.
• Collaborate with platform teams to ensure robust, secure, and scalable data infrastructure in cloud environments.
• Develop plans to meet requirements, organise a team capable of executing the plans, and lead and track delivery.
• Drive an interview process to enable team growth.
• Maintain a safe and inclusive team environment in which people thrive.
• Operate in a transparent way, communicating clearly and accurately to leadership and the broader organization.
• Develop a high‑performing team through coaching, feedback and ensuring opportunities for growth.

Basic Qualifications

• Graduate studies in Computer Science or Applied Math, undergraduate studies in Computer Science and relevant graduate studies in the life sciences with a focus on AI/ML techniques, or undergraduate studies in Computer Science and equivalent work history. Candidates with graduate studies in CS and biological sciences or equivalent work history will be highly competitive.
• 5+ years of experience applying AI/ML in industry within pharmaceutical, biotech or closely related domains.
• 5+ years experience of professional software development practices: code standards, code review, version control, CI/CD, testing, documentation, Agile, with the ability to mentor others in these practices.
• 2+ years experience in a technical lead or engineering manager role with direct reports.
• Track record as an independent contributor capable of end‑to‑end development of ML‑powered products.
• Advanced Python programming skills and a track record of delivering robust software solutions.
• Proficiency with standard deep learning algorithms and model architectures.
• In depth knowledge in machine learning best practices, scalable training and deployment.
• Experience operating in cloud environments (e.g., Azure, AWS, or GCP).
• Strong product mindset‑focus on user needs, adoption, and measurable value.
• Strong communication skills‑able to translate complex technical concepts into language understandable by clinicians, statisticians, and business leaders.

Preferred Qualifications

• PhD in Machine Learning.
• Peer‑reviewed publications in major AI conferences.
• Experience with foundation models, representation learning, and transfer learning on biomedical data.
• Multimodal modelling across structured, unstructured, and imaging data.
• Time series modelling.