[US] Computational Chemistry Intern (Materials Modeling/Molecular Simulation)

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. Pacific Time Zone (Remote)
  
  
  
  • Candidate must be based in the U.S. West 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