DeepRec.ai
Product Engineer – AI-Driven Materials & Battery Simulation Platform
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.
Role Overview 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.
AI-Driven Simulation and Automation
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.
Battery R&D Integration
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.
Core Competencies
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
Minimum Qualifications
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.
Preferred Qualifications
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.
#J-18808-Ljbffr
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.
Role Overview 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.
AI-Driven Simulation and Automation
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.
Battery R&D Integration
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.
Core Competencies
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
Minimum Qualifications
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.
Preferred Qualifications
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.
#J-18808-Ljbffr