About the Company
A leading energy-technology firm advancing next-generation battery materials and intelligent energy systems. The team is at the forefront of applying modern machine learning to materials discovery, molecular simulation, and high-performance battery development. Their AI-enhanced Li-Metal and Li-ion platforms are among the first to incorporate electrolyte materials discovered through data-driven scientific methods, enabling progress across mobility, energy storage, robotics and aerospace.
What You Can Expect
- Strong compensation and benefits, including meaningful equity in a fast-scaling public company.
- The chance to contribute to an ambitious scientific mission focused on accelerating the transition to cleaner global energy systems.
- A collaborative workplace where AI, computational science and advanced battery R&D converge.
- Significant career growth opportunities working alongside top researchers, engineers and domain experts.
The company is seeking a Product Engineer to design and lead an AI-driven molecular simulation and materials informatics platform supporting the development of next-generation battery materials.
You will connect advanced AI model architectures with computational chemistry, molecular dynamics (MD) and phase-field simulation. This role centers on building and scaling the scientific computing stack that powers materials discovery and battery R&D across the organization.
You will take early-stage AI4Science capabilities — from ML force fields and surrogate models to automated MD pipelines — and turn them into reliable, developer-friendly APIs and internal platforms.
Key Responsibilities
Platform and Architecture
- Lead the full architecture and delivery of a scientific computing platform that unifies AI models, simulation tools and experimental data.
- Build and optimize high-performance simulation services in C++ for large-scale MD, phase-field and related materials models.
- Define and evolve platform interfaces and APIs that expose simulation, data and ML services to internal users.
- Develop and operationalize AI/ML models for materials informatics, including ML force fields, surrogate modeling and uncertainty-aware pipelines.
- Build scalable MD automation systems that manage large batches of simulations, including scheduling, monitoring and data capture.
- Convert cutting-edge research prototypes into production-grade simulation and AI services.
- Collaborate closely with scientists and experimental teams to translate R&D requirements into practical platform features.
- Develop simulation tools supporting analysis of dendrite behavior, degradation pathways and electrolyte/material performance.
- Ensure seamless integration between simulations, experimental workflows and analytics systems.
- Expertise in C++ and scientific/high-performance computing
- Experience with HPC environments and parallel computing (MPI, CUDA, GPU acceleration, or similar)
- Strong knowledge of MD simulations and associated tooling
- API engineering and scalable software/platform architecture
- Understanding of battery materials informatics and AI4Science workflows
- Experience building automated MD workflows and simulation pipelines
- Hybrid background across scientific computing and modern software engineering
- PhD in Materials Science, Computational Physics, Computational Chemistry or a similar field.
- At least 1 year of post-graduate experience in computational materials science, including MD or phase-field simulation.
- Proven ability to build production-grade scientific software in C++ or related systems languages, ideally in HPC environments.
- Hands-on exposure to AI/ML for materials modeling (ML force fields, surrogate models, automated ML workflows).
- Experience developing APIs, services and platforms for use by engineering or scientific teams.
- Strong grounding in algorithms related to materials behaviour (dendrite formation, transport, microstructure evolution).
- Demonstrated ability to work directly with experimentalists and domain scientists.
- Experience developing or scaling AI4Science platforms unifying simulation, ML and laboratory/experimental data.
- Background with cloud-native scientific computing (Kubernetes, containers, workflow engines).
- Prior exposure to battery R&D (Li-metal, Li-ion, electrolytes, interfaces) and multiscale modeling.
- Experience leading product or platform engineering initiatives within deep-tech or research-heavy environments.
- Familiarity with modern data/ML stacks such as Python, PyTorch/JAX/TensorFlow, model registries and workflow orchestration tooling.
