About the Company
A global energy-technology organization developing next-generation Li-Metal batteries for electric mobility across automotive, aviation, and advanced energy applications. This team integrates modern machine learning directly into materials R&D, cell design, manufacturing workflows and safety analytics, operating across major hubs in North America and Asia.
About the Advanced Computation Division
This group serves as the company’s core AI and computational science unit. It brings together computational materials scientists, software engineers and machine learning researchers working hand-in-hand with experimental chemists and product engineers. The team builds intelligent scientific tooling, accelerates materials discovery and supports fast iterative R&D.
About the Molecular Discovery Platform
The company’s flagship platform for AI-accelerated materials discovery analyzes more than 10^8 small molecules across quantum-level, ML-derived and experimentally curated properties. Leveraging GPU-accelerated simulation, large-scale automation and advanced visualization, it enables rapid navigation across vast chemical space.
About the Role
The team is seeking a Senior Computational Materials Scientist to contribute to the development of this platform while advancing simulation capabilities for electrolyte systems, solid electrolyte interphase (SEI) modeling, reaction network methods, force field development and large-scale molecular dynamics acceleration on modern HPC infrastructure.
You will collaborate across computation, software, AI and experimental groups to develop tools that connect quantum chemistry, statistical mechanics and machine learning for practical molecular design.
Key Responsibilities
- Design and execute large-scale quantum chemistry and molecular dynamics simulations using industry-standard tools (e.g., GPU4PySCF, GROMACS, LAMMPS, Gaussian).
- Develop and refine force fields and interatomic potentials for electrolyte-relevant chemistries.
- Build and improve simulation workflows for SEI formation, including reaction network analysis and atomistic modeling.
- Contribute to property-calculation workflows covering key quantum descriptors (HOMO, LUMO, ESP), thermodynamics and kinetics.
- Automate high-throughput simulation pipelines using Python, HPC schedulers (e.g., SLURM) and distributed compute environments.
- Integrate new simulation capabilities into the broader molecular discovery platform through APIs or modular Python packages.
Qualifications
Required
- PhD in Materials Science, Chemistry, Chemical Engineering, Physics or a closely related discipline
- 5+ years of post-PhD experience in computational chemistry or computational materials science
- Hands-on experience with major molecular simulation packages (GROMACS, LAMMPS, Gaussian, VASP, Quantum Espresso, ADF, GPU4PySCF or similar)
- Strong Python skills, including scientific libraries (NumPy, ASE, PySCF etc.) and experience writing reproducible research-grade code
- Experience with high-throughput computation and large-scale data workflows on HPC or GPU clusters
- Strong communication skills and comfort working across experimental, computational and AI teams
- Experience with battery materials, electrolyte systems or solid/liquid interface modeling
- Background in force-field development, reactive MD (Polarizable FF, ReaxFF, MLFF) or coarse-grained simulation
- Familiarity with cheminformatics concepts (molecular representations, fingerprints, exploration of chemical space)
- Contributions to open-source simulation frameworks or published methodology papers
- Experience with unsupervised learning methods (dimensionality reduction, clustering beyond k-means)
- Exposure to CUDA or GPU-accelerated coding
Who Thrives Here
- You enjoy working at the intersection of chemistry, physics, ML and large-scale computation
- You’re comfortable challenging the limits of standard computational tools
- You have a natural curiosity for molecular behavior, electrolyte chemistry and computational design
- You prototype quickly, iterate thoughtfully and value reproducible scientific workflows
- You like building tools that turn raw simulation output into interactive, research-ready platforms
