At Roivant, we are passionate about discovering and developing new drugs to impact patients' lives. Since its inception in 2014, Roivant has launched over 20 portfolio companies (Vants), overseen 5 successful IPOs, established a $3B partnership with a global pharma, built a pipeline of over 40 assets across various modalities and therapeutic areas, and delivered 8 successful phase 3 readouts.
Roivant is currently building new capabilities in drug discovery and expanding its existing development engine to become the world's leading tech-enabled pharmaceutical company. Roivant's drug discovery capabilities are driven by our computational discovery platform, which combines preeminent physics-based tools with deep expertise in machine learning to generate unprecedented predictive power that can tackle previously intractable discovery challenges. The tight integration of this computational platform with our experimental capabilities enables the rapid design and optimization of new drugs to address a wide range of targets for diseases with high unmet need.
We believe that the future of drug discovery lies in integrating predictive sciences, biology, and medicinal chemistry to accelerate the path to new medicines. This role is an opportunity to be an architect of this paradigm shift and generate transformative benefit for patients.
The candidate will lead the machine learning team to develop cutting edge ML methods to substantially advance computation-driven drug discovery. The candidate will work closely with an interdisciplinary team of computational chemists, biophysicists, scientific programmers, and software engineers to develop our computational platform that combines latest advances in machine learning and physics-based models. Competitive pay, equity, strong perks, and a fun working environment, along with the opportunity to do cutting edge science to design better medicines, are all good reasons to join us! Learn more at www.silicontx.com and www.roivant.com. Please find the job description below:
- Assemble and lead a team of machine learning researchers to develop cutting-edge machine learning and statistical models that enable and accelerate drug discovery projects. Research areas include
- Develop protein structure prediction methods combining ML and physics-based models, achieving crystallographic accuracy and enabling prediction of effects of mutations and post-translational modifications
- Collaborate with the force field team to develop and train machine learning models to generate and improve force field parameters; develop ML/statistical models for assessing predictive accuracy of our physics models
- Develop ML/statistical models for predicting molecular properties based molecular structures
- Develop generative models for designing drug-like molecules
- Collaborate with the platform team to deploy the above models in target evaluation and drug discovery projects to enable or substantially accelerate such efforts
- Work with scientific leadership at Silicon-Roivant to develop the strategy for company success through platform technologies
- Recruit top talents, mentor the team, develop individual skills, demonstrate leadership, and support an inclusive culture of scientific innovation
- Master or Ph. D. degree in computer science, applied math, or physical sciences
- Notable accomplishments in machine learning research with strong publication record
- Experience leading a collaborative team.
- Extensive experience with common machine learning tools, such as Scikit.learn, Tensorflow, Pytorch, etc..
- Experiences working with large data sets.
Additional Desirable Qualifications:
- Experience in collaboration with research scientists in applying ML models to solve scientific problems
- Experience with cheminformatic and bioinformatic tools
Roivant Sciences provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.