Join us.


Lead AI Scientist

With over 35 nationalities and a range of backgrounds represented in our Benevolent team, we aim to build an inclusive environment where our people can bring their authentic selves to work, be respected for who they are and the exceptional work they do. We welcome and actively encourage applications from all sections of society and are committed to offering equal employment opportunities regardless of sex, race, religion or belief, ethnic or national origin, marital, domestic or civil partnership status, sexual orientation, gender identity, parental status, disability, age, citizenship, or any other basis. We see our diversity as an asset as we tackle challenging problems that bridge the gap between drug discovery and technology.


The Role

The Genetics team is focused on applying bioinformatics & machine learning techniques to use human genetics to inform and improve drug discovery. We focus on the use of large-scale human genetics datasets to improve precision medicine approaches and target identification. We are keen to hear from Lead Machine Learning Researchers, with a passion and proven track record of integrating and analysing genetic datasets. You will find yourself working as part of a cross-functional team supporting BenevolentAI’s data integration, target identification and precision medicine activities. You will report to the SVP of AI.

Primary Responsibilities

  • Develop sophisticated machine learning models to understand the genetic basis of complex diseases. Considering unsupervised and supervised approaches.
  • Process and integrate genomic datasets (Hi-C, ATAC-seq, scRNA-seq, genotype calls, WES, WGS) from large patient cohorts for refined patient endotyping and target identification.
  • Handle and model clinical data representations (e.g. ICD9/10, READ, PheCODEs etc, natural language, quantitative and binary outcomes).

We are looking for someone with

  • A PhD in machine learning, computational biology or ML applied to genetics, genomics or related fields of research
  • Postdoctoral or industry experience (5+ years).
  • Clear publication record in Machine Learning and/or genetics & genomics.
  • Strong programming ability in relevant frameworks and languages such as Python, PySpark and associated libraries (Pytorch/Pyro, Tensorflow/TFP, sklearn, stan, etc).
  • A deep understanding of human genetics, specifically the application of GWAS to understand disease cause and risk.
  • Excellent communicator, both verbal and written, with an ability to share knowledge and ideas between ML, translational science and engineering disciplines.
  • Hands-on experience working with genetics/genomics data types such as genotyping calls, whole exome sequencing (WES), Whole genome sequencing (WGS) and GWAS summary statistics.
  • Experience in handling clinical datasets and resources such as UK Biobank.
  • Experience with DNAnexus / WDL / Other workflow development languages.
  • Strong background and understanding of different NGS technologies.

We share a passion for being part of a mission that matters, and we are always looking for curious and collaborative people who share our values and want to be part of our journey.  If that sounds like a fit for you, hit the apply button and join us.

About us

BenevolentAI (AMS: BAI) is a leading, clinical-stage AI-enabled drug discovery and development company listed on the Euronext Amsterdam stock exchange. Through the combined capabilities of its AI platform, scientific expertise, and wet-lab facilities, BenevolentAI is well-positioned to deliver novel drug candidates with a higher probability of clinical success than those developed using traditional methods. The Benevolent Platform™ powers a growing in-house pipeline of 13 named drug programmes and over 10 exploratory programmes, and it maintains successful collaborations with AstraZeneca, as well as leading research and charitable institutions. BenevolentAI is headquartered in London, with a research facility in Cambridge (UK) and a further office in New York.

Want to do a little more research before you apply?

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