Battery Simulation & Computational Scientist Internship

• Commit to a 6+ month internship (Remote or Onsite); this is not a position for students only available for summer months. • Analyze electrochemical data to build, parameterize, and apply new numerical models. • Fit electrochemical models to experimental data and evaluate model performance across a range of different electrochemical test techniques. • Support infrastructure development, helping to build the backend pipelines and tools that bring physics-based models to a wider audience. • Work closely with scientists and cross-functional teams to discover and communicate knowledge for the long-term development of our next-generation battery materials. • Invest in our values and champion them as we grow.

Are you a thoughtful, creative, and code-savvy simulation scientist or engineer? We are looking for someone who sits at the intersection of computer science and electrochemistry. You might be a software engineer with a passion for physics, or a theorist with a talent for building robust software infrastructure. Sila Nanotechnologies is seeking a Battery Simulation & Computational Scientist (Intern) to: • Architect and implement physics-based battery models using PyBaMM. • Bridge the gap between theoretical equations and production-grade software code. • Leverage key insights from our battery models to accelerate learning cycles and reduce the cost of empirical R&D. • BSc (or preferably MSc/currently enrolled in a Ph.D. program) or equivalent research experience in Computer Science, Applied Mathematics, Chemical Engineering, Materials Science, or a related field. • Strong experience using PyBaMM (Python Battery Mathematical Modeling) is required. • Mastery of Python, including standard scientific libraries (NumPy, SciPy, Pandas). • Experience interacting with databases using SQL or Snowflake. • Proficient with Git and collaboration platforms (GitHub or GitLab). • Ability to navigate and execute workflows in Linux or macOS terminal environments. • Knowledge of electrochemical theory and the application of the P2D (Pseudo-Two-Dimensional) or Doyle-Fuller-Newman models. • Public contributions to the PyBaMM repository or related sister projects on GitHub. • Proficiency in using AI coding tools (e.g., Claude Code, Copilot) and knowledge of effective prompting techniques to accelerate development. • Experience building CI/CD pipelines, containerization (Docker), or some exposure to full-stack development.