Computational Chemist

Job Summary

To support the PFAS substitution program, the full-time remote Computational Chemist will design and run high-throughput quantum chemistry and molecular dynamics campaigns, manage machine learned force fields, and collaborate with cross-functional teams to validate and deploy predictive models.

Key Responsibilities

• Design and execute high-throughput simulation campaigns to generate training data for PFAS chemical space
• Develop automated workflows for data generation and ensure reproducibility and provenance of datasets
• Train, validate, and deploy machine learned interatomic potentials tailored for PFAS substitution needs

Required Qualifications

• PhD in Computational Chemistry, Materials Science, Physics, Machine Learning, or a related field with specialization in machine learned interatomic potentials
• Experience developing and deploying machine learned force fields, including training and validation of at least one potential
• Hands-on experience with quantum chemistry and electronic structure codes for generating first principles training data
• Strong programming skills in Python and experience with modern ML frameworks such as PyTorch or JAX
• Practical experience with molecular dynamics engines and classical force field selection for organic and condensed phase systems