Product Engineer, AI Battery Simulation

Product Engineer – AI-Driven Materials & Battery Simulation Platform AI & Advanced R&D About SES AI SES AI Corp. (NYSE: SES) is dedicated to accelerating the world’s energy transition through groundbreaking material discovery and advanced battery management. We are pioneering the integration of cutting‑edge machine learning into battery R&D, and our AI‑enhanced, high‑energy‑density and high‑power‑density Li‑Metal and Li‑ion batteries are the first in the world to utilize electrolyte materials discovered by AI. This powerful combination of “AI for science” and material engineering enables batteries for transportation (land and air), energy storage, robotics, and drones—truly taking us Beyond Li‑ion™. Highly competitive salary and robust benefits, including comprehensive health coverage and attractive equity/stock options in a NYSE‑listed company. The opportunity to contribute directly to a meaningful scientific mission: accelerating the global energy transition with clear and broad public impact. A dynamic, collaborative environment at the intersection of AI, computational science, and advanced battery technology. Significant opportunities for growth as you collaborate with leading experts in AI, R&D, and engineering. Role Overview We are seeking a Product Engineer to architect and lead our AI‑driven molecular simulation and materials informatics platform for next‑generation battery materials. This leader will bridge advanced AI model architectures with computational chemistry, molecular dynamics (MD), and phase‑field simulation, designing and scaling the scientific computing platforms and toolchains that power SES AI’s materials discovery and battery R&D. You will support the productization of AI4Science capabilities—from ML force fields and surrogate models to automated MD workflows—turning them into robust, developer‑friendly APIs and platforms used across SES’s global R&D organization. Key Responsibilities Lead the end‑to‑end design of a scientific computing platform for battery materials, integrating AI/ML models, experimental data, and cloud infrastructure. Architect and implement high‑performance computing (HPC) and C++‑based simulation services for large‑scale MD, phase‑field, and related materials models. Define and evolve the API architecture and platform interfaces that expose simulation, ML, and data services to internal users and tools. AI-Driven Materials Simulation & Automation Drive development and deployment of AI/ML models for materials informatics, including ML force fields, surrogate models, and uncertainty‑aware prediction pipelines. Build and manage MD automation systems and simulation toolchains that scale across thousands of runs, integrating job scheduling, monitoring, and data management. Translate research prototypes (e.g., new ML models for materials, dendrite growth algorithms) into reliable, production‑grade services. Battery R&D Integration Work closely with battery scientists, electrochemists, and experimentalists to map R&D needs into platform features and workflows. Develop simulation and algorithmic capabilities focused on battery dendrite growth, degradation mechanisms, and electrolyte/material performance. Ensure tight integration of simulation and AI outputs with experimental workflows, data pipelines, and informatics dashboards. Core Competencies C++ and high‑performance/scientific computing HPC systems and parallel computing (MPI, CUDA, GPU acceleration, or similar) Molecular dynamics (MD) simulation and related tools/workflows API engineering and large‑scale platform architecture Battery informatics and AI4Science for materials MD workflow automation & simulation toolchain design Hybrid expertise in scientific computing + modern software engineering Minimum Qualifications PhD in Materials Science, Computational Physics, Computational Chemistry, or a closely related field. 1+ years (post‑graduate) of experience in computational materials science, including molecular dynamics and/or phase‑field simulation. Proven track record building production‑grade scientific software in C++ and/or similar systems languages, ideally in an HPC context. Hands‑on experience with AI/ML for materials informatics, such as ML force fields, surrogate models, or automated ML workflows. Demonstrated experience designing and implementing APIs, services, and platforms used by other engineers or scientists. Strong background in algorithm development related to materials behavior (e.g., dendrite growth, transport, microstructure evolution). Experience collaborating closely with experimentalists and domain scientists and translating their needs into robust engineering solutions. Preferred Qualifications Experience building or leading AI4Science platforms (e.g., integrating simulation, ML, and lab/experimental data into unified systems). Prior ownership of cloud‑native scientific computing platforms (Kubernetes, containers, workflow engines, etc.). Background in battery R&D (Li‑metal, Li‑ion, electrolytes, interfaces) and associated multiscale modeling. Experience leading product engineering or platform engineering teams, especially in deep‑tech or R&D‑heavy environments. Familiarity with modern data and ML stacks (Python, PyTorch/JAX/TensorFlow, feature stores, model registries, workflow orchestration). #J-18808-Ljbffr