AI/ML Engineer

About the Role

At GSK, we envision a future where advanced Machine Learning and AI applications drive the development of transformative medicines, leveraging genetics, functional genomics, and machine learning. AI will also be pivotal in how we diagnose and utilize medicines, empowering individuals to achieve more, feel better, and live longer. This ambitious vision necessitates the creation of products at the forefront of Machine Learning and AI.

The potential for machine learning extends across various business domains, including medicine safety, manufacturing, and supply chain. To capitalize on these opportunities, GSK has established a global Artificial Intelligence and Machine Learning group (AI/ML) with locations in London, San Francisco, Boston, Philadelphia, and Heidelberg. This group is dedicated to the development and application of machine learning to critical problems at GSK. We offer a world-leading data and computational environment, including specialized hardware, to facilitate large-scale scientific experiments that leverage GSK's unique data access.

The AI/ML group actively engages with the machine learning community and publishes its research, code, and models built on public data, operating at the cutting edge of machine learning research. We are seeking a passionate AI/ML Engineer who is eager to apply their talents to active learning in the healthcare sector. You will be working in a research-focused team to build products centered on active learning and creating data-efficient models, supporting multiple large-scale projects within AI/ML. Additionally, the Engineer will gain insights into the pharmaceutical industry and software engineering, translating their research into tools that aid the discovery and development of transformational medicines and vaccines.

Basic Qualifications

• A minimum of an MSc in machine learning, computer science, physics, or a related quantitative field.
• Understanding and application of best practices in Machine Learning, with breadth across the ML stack.
• Experience with machine learning literature and state-of-the-art modeling approaches.
• Previous experience in 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.
• Experience in at least one major deep learning framework (PyTorch, TensorFlow).

Preferred Qualifications

• PhD in machine learning, computer science, physics, or a related quantitative field.
• Relevant scientific publications in AI/ML.
• Understanding and application of best practices in machine learning, software engineering, and/or production deployment of ML services.
• Track record of contributing to open-source projects.
• Understanding of modern ML Architectures, Platforms, and backend systems.
• Mentality of commit early and often, metrics before models, and shipping high quality production code.