At Roivant, we are passionate about discovering & 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 & therapeutic areas, & delivered 8 successful phase 3 readouts.
Roivant is currently building new capabilities in drug discovery & expanding its existing development engine to become the worlds leading tech-enabled pharmaceutical company. Roivants 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 & 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, & medicinal chemistry to accelerate the path to new medicines. This role is an opportunity to be an architect of this paradigm shift & generate transformative benefit for patients.
Roivant Discovery, a drug discovery company headquartered in Boston with offices in New York City, is looking for researchers and programmers in scientific computing with extensive experience in scientific programming, algorithm research, machine learning, or high-performance computing to join our platform team. Working closely with other platform team members, the candidate will develop & optimize advanced physics-based computational codeincluding but not limited to molecular dynamics simulations, free energy calculations, quantum chemistry, and machine learning modelsto solve critical issues in drug discovery. 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!
- Developand implement in proprietary scientific software librariesalgorithms to enable new molecular physics and machine learning models. Active research areas include, but are not limited to:
- Collaborate with quantum chemists to develop machine learning models that predict accurate molecular energies at a fraction of the computational cost of quantum chemistry
- Collaborate with the molecular physics team to develop & implement new molecular interaction models within molecular simulation codes.
- Collaborate with the force field team to develop & train machine learning models to generate & improve force field parameters.
- Collaborate with the advanced simulations team to develop & implement new models to predict biophysical observables from molecular simulations & to guide the simulations with biophysical data
- Collaborate with the advanced simulations team to implement new enhanced sampling methods
- Develop & implement algorithms to accelerate our physics-based simulations & analysis
- Develop robust, scalable software that implement state-of-the-art algorithms in computational chemistry
- Optimize our scientific code for modern high performance & parallel computing architecture
- Highly motivated to develop computational methods and software for discovering better medicines
- B.S., M.S., or Ph.D. in computational physics/chemistry, physical chemistry/chemical physics, applied mathematics, computer science, or related fields
- Extensive programming experience in implementing and optimizing numerical methods (C/C++ & Python preferred)
- Excellent communication skills & strong team player
Additional Desirable Qualifications:
- Experience working with a diverse team on an ambitious project
- Experience in molecular dynamics simulations, Monte Carlo simulations, or other computational physics simulations
- Experience developing machine (such as random forests, support vector machines, deep neural networks) models, familiar with latest developments in machine learning methods.
- Extensive experience with common machine learning tools, such as Scikit.learn, Tensorflow, Pytorch, etc..
- Experience with high performance computing & parallel programming (e.g. MPI, openMP, CUDA)
- Experience in statistical modeling or bioinformatics