Research Scientist (Machine Learning), London at Isomorphic Labs | Torre

Research Scientist (Machine Learning), London

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Full-time

Legal agreement: To be defined

USD75.4K - 100K/year

~COP150M - 200M/year

+ Equity

+ Bonuses

location_on
Hybrid (London)
Posted almost 3 years ago

Responsibilities


Isomorphic Labs is a new Alphabet company that is reimagining drug discovery through a computational- and AI-first approach. We are on a mission to accelerate the speed, increase the efficacy and lower the cost of drug discovery. Come and be part of a multi-disciplinary team driving groundbreaking innovation and play a meaningful role in contributing towards achieving ambitious goals, while being a part of an inspiring, collaborative and entrepreneurial culture. As a Research Scientist in machine learning (ML), you will play an exciting role in building greenfield machine learning based models and algorithms that will power our platform to transform the drug discovery world as we know it. You will partner with leading engineers and scientists to conceive, design, and develop cutting edge machine learning algorithms to unlock new modeling and predictive power which will be critical to the organization’s success. Your work will contribute to the research directions in machine learning by using your extensive knowledge of the field to apply world-leading ML algorithms to drug discovery. You will also have the opportunity to provide technical mentorship and guidance to the ML research community, advising on projects, and shaping the research roadmap based on your deep technical expertise. Responsibilities: - Contribute to our research directions in machine learning by using your extensive knowledge of the field to apply world-leading ML algorithms to drug discovery. - Identify and create novel ML techniques and the required data to train. - Develop the architectures and training algorithms of machine learning models. - Analyse and tune experimental results to inform future experimental directions. - Implement and scale training and inference engineering frameworks. - Report and present research findings and developments clearly and efficiently, to both other ML scientists and scientists of different disciplines. - Iterate collaboratively with scientists and domain experts, sharing your own domain experience. - Suggest and engage in team collaborations to meet ambitious research goals.