Senior Scientist, Computational Chemistry/ML

Title: Senior Scientist

Location: Boston, MA

Company Summary:

Currently partnered with a leading biotech incubator looking for a highly skilled computational scientist to develop and deploy cutting-edge molecular simulation technologies. This is a hands-on role requiring expertise in algorithm development, molecular dynamics, and machine learning applications. You will pioneer efforts to create scalable, production-level modeling pipelines that redefine how molecular interactions are understood and utilized in drug discovery.

Responsibilities:

• Develop and implement advanced methods for molecular simulation, including AI-enhanced molecular dynamics and machine-learned force fields.
• Build robust, scalable cloud-based workflows for modeling biomolecular systems such as proteins, peptides, nucleic acids, and small molecules. **small molecules strongly preferred**
• Drive the integration of novel algorithms into existing computational pipelines to accelerate therapeutic development.
• Collaborate with interdisciplinary teams to address complex R&D challenges, ensuring computational tools are aligned with project needs.
• Validate, benchmark, and deploy innovative approaches for enhanced sampling, free energy calculations, and structure prediction.

Qualifications:

• Ph.D. in Computational Chemistry, Biophysics, or related field with a focus on molecular simulation.
• Proven track record of developing and implementing novel algorithms for biomolecular simulation.
• Strong programming skills in languages like Python or C++, with experience in molecular simulation tools (e.g., OpenMM, GROMACS, AMBER, CHARMM)
• Familiarity with protein interactions such as:
  • DiffDock, Autodock, Dock, Rosetta, RoseTTAFold-AllAtom
• Experience with machine learning frameworks (PyTorch, JAX) and cloud computing environments (AWS, Docker, Kubernetes).
• Expertise in AI-derived or polarizable force fields, enhanced sampling techniques, and free energy calculations.
• Demonstrated ability to scale computational workflows and optimize pipelines for large datasets.