The AI Accelerator - Job Description

The AI Accelerator is a brand-new, London-based hub, within Computational Innovation, comprising computational biology, human genetics, data and AI expertise.

The purpose of the AI Accelerator is to provision production-quality, versatile, foundational biomedical AI capabilities that can be adapted and deployed to improve and accelerate portfolio decision-making, furthering understanding of the biology driving patient outcomes and identifying mechanisms that increase probability of success.

To achieve this purpose, the AI Accelerator is organised around three key capabilities - AI Systems, focussing on neural methods, AI Reasoning, focussing on causality and knowing why, and AI Enablement, powering AI Systems and AI Reasoning with big data provisioning, MLOps and AI infrastructure.

AI Systems

will exploit neural-based methods to integrate data and impute and infer across the biomedical landscape. It could be electronic health records and medical imaging to support patient segmentation. It could be 'omics data to understand gene regulation and identify novel therapeutic concepts; it could be predicting transcriptional change for a given disease-causing variant.

AI Reasoning

will supplement AI Systems by focussing on developing approaches to identify cause and effect, predict the outcome of an intervention, provide a rationale for a model decision. It could be providing causal structure to multi-omics data; it could be providing orthogonal rationale to a black-box model generated hypothesis; it could be identifying features that are driving model predictions.

AI Enablement

will support AI Systems and AI Reasoning by provisioning harmonised multimodal data for distributed training, managing the model lifecycle and partnering with the IT organisation to ensure AI Systems, AI Reasoning and downstream beneficiaries have the necessary infrastructure and tooling to prototype, implement, adapt and deploy AI capabilities to advance the portfolio.

None of the above can be achieved without people. We are looking for AI scientists, machine learning engineers, research engineers, data engineers, MLOps engineers and AI infrastructure engineers from senior up to senior staff level and people leaders in AI science and engineering.

If you are passionate about using your talents to build state-of-the-art AI approaches that will advance the portfolio and skilled in the required disciplines, please register your interest.

Apply Now to register interest by joining our AI Accelerator talent pool.

Please note that this is a talent community, not a particular recruitment process. There will be no follow up communication on this application. By applying you agree to being contacted by us, should there be a new opening in the future, matching your professional profile and/or interest in a particular position stated by you in your application form.

All qualified applicants will receive consideration for employment without regard to a person's actual or perceived race, including natural hairstyles, hair texture and protective hairstyles; color; creed; religion; national origin; age; ancestry; citizenship status, marital status; gender, gender identity or expression; sexual orientation, mental, physical or intellectual disability, veteran status; pregnancy, childbirth or related medical condition; genetic information (including the refusal to submit to genetic testing) or any other class or characteristic protected by applicable law.

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The AI Accelerator is a brand-new, London-based hub, within Computational Innovation, comprising computational biology, human genetics, data and AI expertise. The purpose of the AI Accelerator is to provision production-quality, versatile, foundational biomedical AI capabilities that can be adapted and deployed to improve and accelerate portfolio decision-making, furthering understanding of the biology driving patient outcomes and identifying mechanisms that increase probability of success. To achieve this purpose, the AI Accelerator is organised around three key capabilities - AI Systems, focussing on neural methods, AI Reasoning, focussing on causality and knowing why, and AI Enablement, powering AI Systems and AI Reasoning with big data provisioning, MLOps and AI infrastructure.

AI Systems will exploit neural-based methods to integrate data and impute and infer across the biomedical landscape. It could be electronic health records and medical imaging to support patient segmentation. It could be 'omics data to understand gene regulation and identify novel therapeutic concepts; it could be predicting transcriptional change for a given disease-causing variant.
AI Reasoning will supplement AI Systems by focussing on developing approaches to identify cause and effect, predict the outcome of an intervention, provide a rationale for a model decision. It could be providing causal structure to multi-omics data; it could be providing orthogonal rationale to a black-box model generated hypothesis; it could be identifying features that are driving model predictions.
AI Enablement will support AI Systems and AI Reasoning by provisioning harmonised multimodal data for distributed training, managing the model lifecycle and partnering with the IT organisation to ensure AI Systems, AI Reasoning and downstream beneficiaries have the necessary infrastructure and tooling to prototype, implement, adapt and deploy AI capabilities to advance the portfolio.

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