Machine Learning Research Scientist/Senior Machine Learning Research Scientist, Structure and Simulation (AI for Drug Discovery)
GenentechAdvances in AI, data, and computational sciences are transforming drug discovery and development. Roche’s Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities. The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.
The Opportunity
At Roche's AI for Drug Discovery (AIDD) group (Prescient Design), We are building state-of-the-art foundation models and scalable systems to fundamentally transform how large and small molecule therapeutics are designed.
We are looking for exceptional machine learning scientists who want to perform high quality research at the intersection of machine learning, structural biology, and physical sciences to directly accelerate drug discovery.
In this role, as a ML Scientist you will:
● Design and train foundation models at scale to answer challenging research questions in large-molecule drug discovery and protein engineering.
● Leverage massive structural biology and biophysical datasets, building novel architectures that capture complex geometric and physical priors.
● Contribute to publications and present scientific findings at internal and external venues.
● Solve real, pressing problems in drug discovery that enable new portfolio capabilities
In this role, as a Senior ML Scientist you will:
● Design and train foundation models at scale to answer challenging research questions in large-molecule drug discovery and protein engineering.
● Leverage massive structural biology and biophysical datasets, building novel architectures that capture complex geometric and physical priors.
● Contribute to cross-functional research teams across the Computational Sciences Center of Excellence.
● Drive publications and present scientific findings at internal and external venues.
● Solve real, pressing problems in drug discovery that enable new portfolio capabilities
Who you are
● PhD degree in Computational Biology, Computer Science, Chemistry, Physics or related disciplines, with up to 2 years of industry research experience (Scientist) or 2+ years of industry research experience (Senior Scientist).
● Demonstrated experience with Python and deep learning libraries such as PyTorch and/or JAX.
● Demonstrated experience architecting and training deep learning models, particularly utilizing modern approaches (e.g., multimodal representation learning, geometric deep learning, and diffusion models).
● Expertise in molecular dynamics simulations and classical force fields (e.g., AMBER, CHARMM, OpenFF), as well as hands-on experience with molecular modeling tools (e.g., OpenMM, Rosetta).
● Demonstrated research experience, including at least one first author publication (or equivalent).
● Strong communication and collaboration skills
● Public portfolio of computational projects (available on e.g. GitHub)
Relocation benefits are NOT available for this job posting
The expected salary range for this position, based on the primary location of New York for the ML Scientist, is $141,100 - 262,100, and for the Senior ML Scientist, is $160,900 - 298,700. The expected salary range for this position, based on the location of California for the ML Scientist, is $147,600 - 274,000, and for the Senior ML Scientist, is $168,100 - 312,300. 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.
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