Machine Learning Force Field Scientist

Responsibilities

• Build and manage large data sets generated using quantum chemical methods at scale to develop predictive ML force fields
• Develop software that trains and applies ML force fields to challenging problems in life and materials sciences
• Extend the accuracy, capability and generalization of current ML force fields
• Communicate results and present ideas to the team
• What you should have:
• A PhD (or extensive experience) in Chemistry, Materials Science, Engineering, Computer Science, or Physics
• A proven track record of scientific contribution and independent research
• Prior experience with development of ML force fields and/or electronic structure methods
• Pay and perks:
• Sound exciting? Apply today and join us!

Benefits

Additional Information

We're seeking a Machine Learning (ML) Scientist to join us in our mission to transform the discovery of therapeutics and materials. Schrödinger has pioneered a physics-based software platform that enables discovery of high-quality, novel molecules for drug development and materials applications more rapidly and at lower cost compared to traditional methods. The software platform is used by biopharmaceutical and industrial companies, academic institutions, and government laboratories around the world. Our multidisciplinary drug discovery team also leverages the software platform to advance collaborative programs and its own pipeline of novel therapeutics to address unmet medical needs. As a member of our Machine Learning team, you'll develop state-of-the-art ML force fields targeting impactful applications in Life and Materials sciences. Who will love this job: An ML force fields expert who has developed, validated, and applied ML force fields to simulate complex condensed-phase systems, such as solid-liquid interfaces, reactive events in the condensed phase, or solvated biomolecules. An innovator who's driven to leverage technical knowledge to make a tangible impact A scientist with deep knowledge of both finite system and periodic DFT, as well as other electronic structure methods, and who understands the limitations and appropriate applications of these methods A proficient Python programmer with prior knowledge of ML toolkits such as PyTorch, Scikit-Learn, NumPy, SciPy, and Pandas An independent researcher who enjoys collaborating with an interdisciplinary team in a fast-paced environment

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Company Intel

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