Machine Learning Engineer

London, England, United Kingdom · Engineering

Description

BenevolentAI, founded in 2013, creates and applies AI technologies to transform the way medicines are discovered and developed. BenevolentAI seeks to improve patient’s lives by applying technology designed to generate better data decision making and in doing so lower drug development costs, decrease failure rates and increase the speed at which medicines are generated. The company has developed the Benevolent Platform™ - a discovery platform used by BenevolentAI scientists to find new ways to treat disease and personalise drugs to patients.

BenevolentAI is HQ’d in London with a research facility in Cambridge (UK) and further offices in New York and Antwerp. BenevolentAI has active R&D drug programmes from discovery to Phase II in disease areas such as ALS, Parkinson’s, Ulcerative Colitis and Sarcopenia.


The Role

The NLP team is responsible for extracting relevant biomedical information from scientific literature. We use state of the art NLP methods to extract entities, find relations and represent this information to downstream AI applications and drug discovery scientists.


In this role, you will work closely with AI researchers to develop, deploy and maintain machine learning systems. You will do this by using compute clusters that are accessed via k8s and/or dedicated machine learning deployment systems, like Polyaxon.


Primary responsibilities


We’re looking for someone with...

Who are we?

We have assembled a diverse, exceptionally talented and spirited team to tackle the most pressing and challenging problems at the intersection of artificial intelligence and drug discovery. We bring our ideas and passion for new technology and medicine discover to life by questioning traditional scientific dogmas.

Our core values reflect who we are and how we work and they are so important to achieve our mission: Bring better medicine to patients faster.

Put patients first. Drive to delivery. Break boundaries. Own the solution.

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