What are the responsibilities and job description for the [US] Computational Chemistry Intern (Materials Modeling/Molecular Simulation) position at SES AI?
Computational Chemistry Intern (Materials Modeling / Molecular Simulation)
About Us
SES AI is a leader in AI-driven materials discovery, building the Molecular Universe (MU) platform to accelerate the development of next-generation battery chemistries. Our work integrates physics-based simulations, machine learning, and large-scale data infrastructure to enable rapid innovation in material science with a dedication to AI for Science.
To learn more about SES, please visit: www.ses.ai
Position Scope
SES AI is seeking a Computational Chemistry Interns to join the Molecular Universe team and support computational modeling and simulation of advanced electrolyte systems. This is a hands-on research role focused on liquid-phase molecular dynamics (MD) simulations, especially for electrolyte systems relevant to next-generation batteries.
Interns will receive training and mentorship from our computational scientist, and collaborate across global teams.
- Location: U.S. Eastern Time Zone (Remote)
- Candidate must be based in the U.S. East Coast region to support business operations.
- Duration: 6 months
Responsibilities
- Contribute to the SES Molecular Universe project by supporting computational chemistry modeling and simulation of advanced electrolyte systems
- Independently or collaboratively perform molecular dynamics simulations for liquid-phase systems, especially electrolytes, including system construction, initial structure generation, and simulation parameter setup
- Execute the full MD workflow, including job submission, HPC resource utilization, run monitoring, troubleshooting, and issue resolution
- Analyze simulation results in depth, including but not limited to:
- Structural properties such as radial distribution functions (RDF), coordination numbers, and solvation structures
- Dynamic properties such as diffusion coefficients and ion transport behavior
- Thermodynamic and statistical property extraction
- Build and improve automated data-processing pipelines to enhance simulation efficiency, reproducibility, and scalability
- Convert simulation outputs into clear reports, visualizations, and presentations that support scientific and engineering decision-making
- Collaborate with internal teams to improve workflow robustness and reproducibility across simulation pipelines
- Support the scaling and engineering of molecular simulation workflows within the MU platform
Preferred / Advanced Responsibilities
- Contribute to force field development, optimization, and validation for electrolyte or ion-containing systems
- Explore higher-accuracy or higher-efficiency simulation methodologies
- Participate in the engineering and platformization of simulation workflows, including workflow automation, orchestration, and task scheduling
Qualifications
- PhD (or PhD candidate) in Computational Chemistry, Materials Science, Chemical Engineering, Physical Chemistry, or a related field
- Hands-on experience with molecular dynamics simulations, particularly for liquid-phase systems
- Familiarity with common simulation tools such as GROMACS, LAMMPS, OPENMM, or similar packages
- Experience with electrolyte systems, ionic systems, battery-related simulations, or sodium-ion systems is strongly preferred
- Understanding of molecular force fields, including basic principles of force field development and parameterization; direct experience is preferred
- Programming skills in Python or similar languages for data analysis, workflow automation, and simulation pipeline development
- Strong problem-solving skills and the ability to diagnose simulation instability, convergence issues, and physical inconsistencies
- Excellent communication skills, with the ability to clearly present technical findings to both technical and non-technical audiences
- Ability to work effectively in a collaborative, international research environment
Language Requirement
- Professional English proficiency is required, including technical discussions, documentation, and presentations
Why Join SES AI
- Work on real, high-impact problems in next-generation battery materials discovery
- Contribute to production-relevant simulation workflows rather than isolated academic projects
- Gain exposure to the intersection of molecular simulation, automation, AI for Science, and materials innovation
- Collaborate with a global team across simulation, machine learning, and experimental validation