Machine Learning Force Fields Scientist (Materials Science)

Key Skills

Analytical and Problem solving, Machine learning techniques, Neural Networks, NumPy, Pandas, Python Programming, PyTorch, Scikit-learn, SciPy, Statistics

Job Description

We’re seeking a Machine Learning (ML) Scientist to join us in our mission to improve human health and quality of life through the development, distribution, and application of advanced computational methods.

As a member of our Materials Science team, you’ll work closely with our Machine Learning and Applications Science teams to develop new hybrid simulation frameworks combining ML, density functional theory (DFT) and atomistic simulations to provide significant advantages in efficiency and accuracy for critical property predictions impacting applications in optoelectronics, catalysis, energy storage, semiconductors, aerospace, and specialty chemicals. 

Who will love this job:

• A statistical and machine learning expert with robust problem-solving skills
• A scientist with understanding of current limitations of force fields and how to test their boundaries for real world applications
• An innovator with deep knowledge of DFT and other electronic structure methods who understands the limitations and appropriate applications of these methods
• A materials science enthusiast who’s familiar with ASE or other informatics packages for materials science applications
• A proficient Python programmer who knows machine learning packages like Scikit-Learn, NumPy, SciPy, Pandas, and PyTorch
• An independent researcher who enjoys collaborating with an interdisciplinary team in a fast-paced environment

What you’ll do:

• Research and analyze large data sets generated using quantum chemical methods at scale to develop predictive machine learning models, both for direct training and feature generation for calibration models.
• Communicate results and present ideas to the team
• Develop tools and workflows that can be integrated into commercial software products
• Work with customers on various machine learning-centric materials science research projects 
• Validate existing Schrödinger machine learning products using public data sets or internally generated data sets

What you should have:

• A PhD (or extensive experience) in Chemistry, Materials Science, Engineering, Computer Science, or Physics
• Hands-on experience with the application of machine learning, neural networks, deep learning, data analysis, or chemical informatics to materials and complex chemicals R&D

Pay and perks:

Schrödinger understands it’s people that make a company great. Because of this, we’re prepared to offer a competitive salary, stock options, and a wide range of benefits that include healthcare (with dental and vision), a 401k, pre-tax commuter benefits, a flexible work schedule, and a parental leave program. We have catered meals in the office every day, a company culture that is relaxed but engaged, and over a month of paid vacation time. Our Administrative and Human Resources departments also plan a myriad of fun company-wide events. New York is home to our largest office, but we have teams all over the world. Schrödinger is honored to have been selected as one of Crain's New York Best Places to Work for the past three years running.

Estimated base salary range (NYC only): $120,000 - $145,000. Actual compensation package is dependent on a number of factors, including, for example, experience, education, degrees held, market data, and business needs. If you have any questions regarding the compensation for this role, do not hesitate to reach out to a member of our Strategic Growth team.