SES

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

Role Description SES AI is seeking a Computational Chemistry Intern 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. Benefits - 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.