Research Scientist, Soft Matter Simulations
About The Position
Apply soft matter physics and molecular simulation expertise to accelerate the discovery, optimization, and understanding of complex soft matter systems. Use coarse-grained and atomistic modeling, mesoscale simulation, analytical theory, and agentic AI systems to investigate polymers, colloids, surfactants, gels, liquid crystals, and formulated fluid systems relevant to commercial applications. The role operates at the intersection of physics-based simulation, AI/ML, and autonomous scientific workflows. Computational insight will be used to identify promising formulations and material compositions, explain structure-property relationships, guide optimization campaigns, and help agents reason scientifically over simulation and experimental data. Relevant methods include atomistic and coarse-grained molecular dynamics, dissipative particle dynamics, Monte Carlo sampling, self-consistent field theory, field theoretic simulation, and liquid state theory. This is a hands-on research position connecting soft matter and molecular simulation expertise with software development and practical scientific impact. The scientist will collaborate with computational scientists, AI researchers, software engineers, and experimental teams to turn simulations, models, and scientific reasoning into robust software, agentic frameworks, and closed-loop scientific workflows.
Requirements
- PhD in chemical engineering, physics, materials science, chemistry, or a related field, or a master's degree with equivalent experience.
- Strong background in soft matter physics covering polymers, colloids, surfactants, gels, or liquid crystals, with hands-on expertise in atomistic and coarse-grained molecular dynamics.
- Working knowledge of at least two of the following: DPD or other mesoscale methods, Monte Carlo, SCFT, field theoretic simulation, or liquid state theory.
- Strong Python scientific software development skills and fluency with modern practices including version control, testing, code review, and packaging.
- Experience running and automating simulations at scale on HPC or cloud, and integrating computational predictions into experimental or closed-loop optimization workflows.
- Good familiarity with agentic AI systems, autonomous scientific workflows, or simulation-aware agents.
Nice To Haves
- Experience with a compiled language such as C++, Rust, or Fortran for performance-critical simulation code, or GPU acceleration via JAX or PyTorch.
- Contributions to open-source simulation packages such as LAMMPS, HOOMD-blue, GROMACS, or OpenMM.
- Experience developing ML potentials or applying AI/ML to soft matter simulation data and molecular property prediction.
- Experience building agents that reason over or orchestrate simulation workflows, or coupling simulation to active learning and Bayesian optimization pipelines.
- Publication record in soft matter theory and simulation.
Responsibilities
- Implement, extend, and deploy atomistic, coarse-grained, and mesoscale simulation capabilities for soft matter systems, including MD, DPD, SCFT, Monte Carlo, and liquid state theory.
- Engineer simulation software using modern scientific libraries, emphasizing testing, modularity, and reproducibility at scale on HPC and cloud infrastructure.
- Translate simulation outputs into descriptors and datasets for ML models, and build predictive models to guide formulation selection and optimization.
- Develop and deploy agentic AI systems that leverage simulation capabilities for autonomous scientific discovery and closed-loop experimentation.
- Communicate physical insights, model limitations, and recommendations to computational, ML, and experimental collaborators.
Benefits
- Competitive base compensation with bonus potential and generous early-stage equity.
- For full-time U.S. employees: medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company-wide holidays; paid parental leave; educational assistance; commuter benefits, including bike share memberships for office-based employees; and a company-subsidized lunch program.
- For full-time employees outside the U.S.: a comprehensive benefits program tailored to their region.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
Ph.D. or professional degree
