Staff AI/ML Engineer, Controllable Biology

GSK; United Kingdom; On-site; GBP 110,000 - 140,000; Full time

Job summary

GSK in the United Kingdom seeks a Staff AI/ML Engineer - Controllable Biology to advance deep learning models on biological networks and genomic data. You will own end-to-end AI/ML solutions, from data sourcing to deployment, and collaborate across biology, bioinformatics, and software engineering to translate complex biology problems into scalable software.

The role emphasizes ownership, mentoring junior engineers, rigorous experimentation, and delivering robust ML products that support drug

Qualifications

• Master's degree in computer science, mathematics, or a related field.
• 7+ years' experience with deep learning, architectures, scalable training and deployment.
• 7+ years' experience in software development, including code reviews, version control, CI/CD, testing and Agile.
• 5+ years' experience in a technical lead or engineering manager role with direct reports.

Responsibilities

• Convert complex biological questions into tractable mathematical problems solvable by modern computational methods.
• Decompose large problems into quarterly, measurable results and drive delivery through engineering sprints.
• Mentor a multidisciplinary engineering team and guide junior colleagues toward delivery.
• Demo progress frequently, balancing research and engineering velocity.
• Uphold best practices in ML, software engineering, code review and robust development.

Skills

Python

PyTorch

Deep learning

Software development

CI/CD

Model deployment

Machine learning

Team leadership

Education

Master's degree

Tools

Git

Job description

GSK in the United Kingdom seeks a Staff AI/ML Engineer - Controllable Biology to advance deep learning models on biological networks and genomic data. You will own end-to-end AI/ML solutions, from data sourcing to deployment, and collaborate across biology, bioinformatics, and software engineering to translate complex biology problems into scalable software.

The role emphasizes ownership, mentoring junior engineers, rigorous experimentation, and delivering robust ML products that support drug