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SES AI develops lithium-metal batteries for electric vehicles and aircraft, grounded in computational materials science (VASP, LAMMPS, GROMACS) paired with modern ML/AI infrastructure (TensorFlow, PyTorch, LangChain, vector DBs). The tech stack reveals a company bridging physics simulation and production: materials researchers run molecular dynamics; ML engineers deploy models for safety monitoring and design acceleration. Hiring pressure is on engineering and research roles, with active projects spanning simulation automation, electrolyte design, and pilot manufacturing — all pointing to a critical scaling phase from lab validation to commercial production.
Tech Stack 42 technologies
Core StackTensorFlow Python C++ PyTorch scikit-learn NumPy Pandas MLflow Docker Jenkins GitLab CI/CD LangChain Pinecone C# .NET VASP LAMMPS GROMACS FORTRAN Matplotlib RDKit Jupyter Notebook JAX Singularity LlamaIndex Milvus FAISS WPF TCP Modbus+11 more
What SES AI Is Building
◆Challenges
- Automation of simulation pipelines
- Scaling simulation workflows
- Improving reproducibility
- Scaling battery technology from lab to production
- Optimizing pilot-scale manufacturing processes
- Bridging design to commercial readiness
- Accelerating electrolyte design
- Implementing ai in advanced electrolyte r&d
- Scaling commercial electrolyte programs
- Expanding commercial relationships japan korea
▲Active Projects
- Ses molecular universe project
- Molecular universe platform
- Model deployment and engineering
- Online service deployment
- Electrolyte system safety testing
- Next-generation battery platform development
- Customer testing and validation
- Pilot-scale cell manufacturing
- Prototype execution
- Ses ai prometheus team
Hiring Activity
15 roles · 6 in 30d
Department
Seniority
Notable leadership hires: Materials Business Unit Head
Company intelligence
