Sr. ML Scientist, Molecular Dynamics & Structure-based Drug Design

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 Genentech. 

The Opportunity

At Genentech Computational Sciences (gCS) Prescient Design, we are revolutionizing drug discovery with cutting-edge machine learning techniques. We are seeking talented Scientists with a passion for building large-scale, distributed machine-learning algorithms and systems that will transform the drug discovery process.

We are seeking a highly motivated Senior Machine Learning Scientist to join Prescient Design within Genentech Research and Early Development (gRED) 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.

In this role, you will:

• Implement machine learning and computational chemistry-based methods to model protein-ligand interactions.

• Develop machine learning-based enhanced sampling workflows to accelerate protein conformational sampling and protein-ligand binding free energy calculations.

• Integrate machine learning and molecular dynamics to build workflows for cryptic pocket discovery and understanding how small molecules modulate protein conformational ensembles.

• Deploy and deliver technical solutions at the intersection of computational chemistry and software engineering, and machine learning, supporting small molecule design across broader gRED and Roche.

• Closely collaborate with other scientists and researchers within Prescient to build impactful technologies for drug discovery research.

• Build and apply machine learning techniques to biochemical / biophysical datasets and aid in new hypothesis generation with experimental collaborators.

• Collaborate with experimental scientists to design and interpret experiments that validate and refine machine learning-generated hypotheses about novel MOAs.

• 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 have a 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 have a record of scientific excellence as evidenced by at least one publication in a scientific journal or conference.

• You are fluent in Python and experience with scientific software development for biophysical modeling.

• You have a public portfolio of projects available on GitHub/GitLab.

Preferred

• Experience with building OpenMM-based workflows for enhanced sampling.

• Experience with modeling static and dynamic protein-ligand binding interactions.

• Demonstrated experience with modern Python frameworks for deep learning like PyTorch.

• Experience working with biochemical or biophysical datasets including graph, sequence, and structure-based data. 

#gCS

#tech4lifeAI 

Relocation benefits are available for this job posting

The expected salary range for this position based on the primary location of New York is $160,100 - 297,300 and California is $167,400 - $310,800 of hiring range.  Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law.  A discretionary annual bonus may be available based on individual and Company performance.  This position also qualifies for the benefits detailed at the link provided below.

Benefits

Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, a