You will help drive research on Machine Learning for Drug Discovery. The successful candidate will collaborate extensively with computational and experimental scientists and researchers across g. RED to deploy and deliver machine-learning solutions for small-molecule drug discovery. You will implement machine learning and computational chemistry-based methods to elucidate novel mechanisms of action for small molecule drug discovery. You will deploy and deliver technical solutions at the intersec... more details
The Position
A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche.
At Genentech Computational Sciences (gCS) Prescient Design, we are seeking a highly motivated Machine Learning Scientist to help drive research on Machine Learning for Drug Discovery. The successful candidate will collaborate extensively with computational and experimental scientists and researchers across gRED to deploy and deliver machine-learning solutions for small-molecule drug discovery.
The Opportunity
You will help drive research on Machine Learning for Drug Discovery. The successful candidate will collaborate extensively with computational and experimental scientists and researchers across gRED to deploy and deliver machine-learning solutions for small-molecule drug discovery.
You will implement machine learning and computational chemistry-based methods to elucidate novel mechanisms of action for small molecule drug discovery.
You will deploy and deliver technical solutions at the intersection of computational chemistry and machine learning, supporting research directions in molecular design across broader gRED and Roche.
You will closely collaborate with other scientists and researchers within Prescient to build impactful technologies for drug discovery research.
You will build and apply machine learning techniques to biochemical / biophysical datasets and aid in new hypothesis generation with experimental collaborators.
You will collaborate with experimental scientists to design and interpret experiments that validate and refine machine learning-generated hypotheses about novel MOAs.
You will contribute to and drive publications, present results at internal and external scientific conferences, and help make code and workflows open source.
Who you are
You will have a BS, MS, or PhD degree in the physical sciences (e.g. Chemistry, Physics, Chemical Engineering) or quantitative field (?e.g.? Computer Science, Statistics, Applied Mathematics) or equivalent industry research experience.
You will have a record of scientific excellence as evidenced by at least one publication in a scientific journal or conference.
You will be fluent in Python and experience with scientific software development for biochemical modeling (e.g. RDKit, Biopython, MDAnalysis, OpenMM)
You will have demonstrated experience with modern Python frameworks for deep learning like PyTorch
You will have experience working with biochemical or biophysical datasets including graph, sequence, and structure-based data.
You will have a public portfolio of projects available on GitHub/GitLab
Preferred
You will be a good scientific programmer, potentially with "biophysical datasets", and software packages: "BioPython, MDAnalysis, and OpenMM".
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Relocation benefits are available for this posting
The expected salary range for this position based on the primary location of California is $144,900 - 269,800. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors
Benefits
Genentech is an equal opportunity employer, and we embrace the increasingly diverse world around us. Genentech prohibits unlawful discrimination based on race, color, religion, gender, sexual orientation, gender identity or expression, national origin or ancestry, age, disability, marital status and veteran status.
