Machine Learning Force Field Scientist

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 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. What you’ll do: 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: Schrödinger offers a competitive salary, equity-based compensation, and a wide range of benefits including healthcare (with dental and vision), a 401(k), pre-tax commuter benefits, a flexible work schedule, and a parental leave program. The company also provides regular catered meals, a relaxed yet engaged culture, and more than a month of paid vacation. Schrödinger has been recognized by Crain’s New York Best Places to Work, BuiltIn’s NYC Best Place to Work, and Newsweek’s list of America’s 100 Most Loved Workplaces. Estimated base salary range: $120,000 - $175,000. Actual compensation depends on factors such as experience, education, degrees held, market data, and business needs. Sound exciting? Apply today and join us! As an equal opportunity employer, Schrödinger hires outstanding individuals into every position in the company. People who work with us have a high degree of engagement, a commitment to working effectively in teams, and a passion for the company’s mission. We place the highest value on creating a safe environment where our employees can grow and contribute, and refuse to discriminate on the basis of race, color, religious belief, sex, age, disability, national origin, alienage or citizenship status, marital status, partnership status, caregiver status, sexual and reproductive health decisions, gender identity or expression, sexual orientation, or any other protected characteristic. To us, “diversity” isn't just a buzzword, but an important element of our core principles and key business practices. We believe that diverse companies innovate better and think more creatively than homogenous ones because they take into account a wide range of viewpoints. For us, greater diversity doesn't mean better headlines or public images— it means increased adaptability and profitability. #J-18808-Ljbffr Schrödinger