Force Field Developer — Accurate Molecular Models for Drug Discovery and Biomolecular Simulation
The Opportunity
Every molecular dynamics simulation inherits the strengths and the errors of its force field. Getting proteins, ligands, and small molecules right at the level of individual atomic interactions remains one of the hardest open problems in computational chemistry, and it sets a ceiling on how much simulation can contribute to drug design. This role works on raising that ceiling: building more accurate force fields, through physics-based models, machine learning, or both, for drug discovery and the simulation of biomolecules.
The client is a New York–based research company in computational biochemistry and drug discovery that works across disciplines.
The Research Environment
The client pairs specialized simulation hardware it designed in-house with high-performance commodity systems, machine learning, and other computational methods to model molecular behavior at the atomic level and design highly selective drug candidates. Force field work feeds directly into that effort, in close collaboration with the client's drug discovery team, across both in-house programs and partnered projects.
What You'll Do
- Develop physics-based force fields that describe biomolecular systems with greater accuracy.
- Apply machine learning methods to the construction and refinement of force fields.
- Design new parameterization strategies, including approaches for ligands and small molecules.
- Validate and verify force field performance against reference data and established benchmarks.
- Partner with drug discovery scientists so force field improvements carry through to their projects.
- Communicate methods and results clearly to collaborators from other scientific backgrounds.
What You Bring
<>Required
- A Ph.D. in chemistry, chemical physics, computational chemistry, biophysics, or a related field.
- Demonstrated research strength, with a track record of original, accomplished work.
<>Also Valued
- Experience in any of: Physics-Based Force Field Development, Machine Learning for force fields, Parameterization strategy design, Ligand and Small-Molecule Parameterization, or Force Field Verification & Validation.
- A strong command of Physics, Machine Learning, or both, paired with solid knowledge of Chemistry.
- Ability to explain technical reasoning to experts and non-specialists alike, supporting close work with drug discovery colleagues.
- The client weighs research ability and accomplishment more heavily than depth in any single area above.
Compensation & Total Rewards
- Expected annual base salary: $260,000–$400,000, set according to factors such as experience and education.
- Sign-on and year-end bonuses as variable pay.
- A generous benefits package with relocation support.
Joining StaffRight Associates
When you work with StaffRight Associates, you get a recruiting partner who understands the substance of your work and represents it accurately to the teams hiring for it. We focus on matching researchers with problems worth their training, and we'll give you a clear, candid picture of each opportunity before you decide to pursue it.
