AI/ML Engineer

GSK • South San Francisco, CA

23h

About The Position

At GSK we see a world in which advanced applications of Machine Learning and AI will allow us to develop transformational medicines using the power of genetics, functional genomics and machine learning. 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. The opportunities for machine learning extend to many other areas of our business, including medicine safety, manufacturing, and supply chain. To realize these opportunities, GSK has created a global Artificial Intelligence and Machine learning group (AI/ML), with locations in London, San Francisco, Boston, Philadelphia, and Heidelberg, to focus on the development and application of machine learning to problems of critical importance at GSK. We possess a world-leading data and computational environment (including specialist hardware) to enable large-scale, scientific experiments that exploit GSK’s unique access to data. By actively engaging with the machine learning community and publishing our research, code and models built on public data, the AI/ML group operates at the cutting-edge of machine learning research. To help us, we seek a passionate researcher who wishes to turn their talents to the application of causal machine learning to the healthcare sector. You will be working with multiple Research Engineers on building products to support multiple large-scale projects within AI/ML. In addition, the researcher will learn about the pharmaceutical industry and software engineering and translate their research into tools that aid discovery and development of transformational medicines and vaccines. You will have access to outstanding experts in biology, clinical and translational research, chemistry, (software) engineering, data science and machine learning; unrivalled data sources and GSK’s state-of-the-art laboratory and compute infrastructure to help you develop and validate your machine learning research. As a Machine Learning Engineer focusing on applications in oncology, you will be expected to: Design and implement novel scientific approaches for biophysical modeling and foundation model-driven analysis of multi-modal clinical and genomic data for biomarker and target discovery to improve patient selection and enable next-generation assets. Design, develop, and implement analytical solutions using a variety of commercial and open-source tools (common tools include PyTorch and scikit-learn). • Connect and collaborate with subject matter experts in biology, genomics, and medicine. Identify opportunities to apply the latest advancements in Machine Learning and Artificial Intelligence to build, test, and validate predictive models. Develop and embed automated and agentic processes for predictive model validation, deployment, and implementation. Deploy your algorithms to production to identify actionable insights from large databases.

Requirements

• Master’s degree in computer science, applied math, statistics, physics, systems biology, computational biology, bioinformatics, or related field
• Experience in Python programming and knowledge in machine learning, statistics, and applied math.
• Familiarity with modern machine learning methods (generative models, representation learning)
• Experience in building deep learning models, preferably with exposure to biophysical modeling, functional genomics, molecular and cellular biology or to modeling dynamical systems
• Experience with at least one Deep Learning framework such as PyTorch

Nice To Haves

• PhD in computer science, applied mathematics, statistics, physics, systems biology, computational biology, bioinformatics, or a related field.
• Experience in analyzing real-world and/or clinical data.
• Experience in incorporating agentic models into ML workflows
• Understanding of best practices in software engineering, including training and operating algorithms at scale, and production deployment of ML services.
• Knowledge of cancer biology and precision oncology.
• Excellent written and verbal communication skills.
• Ability to digest, synthesize, and implement innovative methods from scientific literature.
• Ability to solve complex problems using creative approaches, state-of-the-art tools, and best engineering practices.
• Ability to work autonomously and collaboratively as part of a team, both teaching and learning every day.
• High impact publications at venues such as NeurIPS, ICML, ICLR etc. would be a plus
• Publicaiton in natural sciences would be a plus

Responsibilities

• Design and implement novel scientific approaches for biophysical modeling and foundation model-driven analysis of multi-modal clinical and genomic data for biomarker and target discovery to improve patient selection and enable next-generation assets.
• Design, develop, and implement analytical solutions using a variety of commercial and open-source tools (common tools include PyTorch and scikit-learn).
• Connect and collaborate with subject matter experts in biology, genomics, and medicine.
• Identify opportunities to apply the latest advancements in Machine Learning and Artificial Intelligence to build, test, and validate predictive models.
• Develop and embed automated and agentic processes for predictive model validation, deployment, and implementation.
• Deploy your algorithms to production to identify actionable insights from large databases.

Benefits

• Available benefits include health care and other insurance benefits (for employee and family), retirement benefits, paid holidays, vacation, and paid caregiver/parental and medical leave.
• In addition, this position offers an annual bonus and eligibility to participate in our share based long term incentive program which is dependent on the level of the role.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level