Machine Learning Engineer Undefined

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, Tel Aviv, and Heidelberg, to focus on the development and application of machine learning to problems of critical importance at GSK. We are now embarking on an AI/ML team in China, to bring cutting-edge solutions to real-world challenges in the healthcare domain. 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 Machine Learning Engineer who wishes to turn their talents to the healthcare sector. You will be working with other engineers on building products to support multiple large-scale projects within AI/ML. In addition, the engineer will learn about the pharmaceutical industry and software engineering and translate their research into tools that aid discovery and development of transformational medicines.

You will have access to outstanding experts in biology, 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 clinical trials and respiratory diseases you will:

• Design, implement, and own AI/ML-driven solutions across the model development life cycle.

• Research and develop state-of-the-art machine learning models, including deep learning, for biomedical prediction tasks.

• Deliver robust, tested, and performant code in an agile environment.

• Collaborate with experts in biology, medicine, and experimentation to optimize data collection for training biomedical ML models

• Develop and embed automated processes for predictive model validation, deployment, and implementation.

• Develop and embed automated processes for predictive model validation, deployment, and implementation.

• Deploy your algorithms to production to identify actionable insights from large databases.

Why you?
Basic Qualifications:
We are looking for professionals with these required skills to achieve our goals:

• A minimum of a 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 and statistics

• Demonstrated proficiency working with biological/healthcare data in an academic or professional setting

• Familiarity with at least one Deep Learning framework such as TensorFlow, Keras or PyTorch

• Proven ability to solve complex problems using creative approaches, state-of-the-art tools, and best engineering practices

• Ability to work both autonomously and collaboratively on complex projects

Preferred Qualifications:
If you have the following characteristics, it would be a plus:

• A PhD in computer science, applied math, statistics, physics, systems biology, computational biology, bioinformatics, or related field

• Academic or industry experience in the biomedical sciences especially in respiratory diseases

• Prior experience working in clinical trials

• Experience with real world evidence or clinical trial data

• Expertise developing machine learning models within the PyTorch framework

• Experience with theory & applications in software engineering; training and operating algorithms at scale; production deployment of ML services

• Ability to digest, synthesize, and implement innovative methods from scientific literature