Applied AI Engineer - Hong Kong

CATL; Hong Kong; On-site; HKD 1,200,000 - 1,800,000; Full time

Job summary

CATL in Hong Kong seeks a researcher to advance AI-powered materials modeling and battery R&D. You will work on multiscale simulations, data-driven discovery, and AI integration across science, engineering and production teams.

The role emphasizes independent tackling of core items while collaborating with cross-disciplinary teams to accelerate material design, battery systems, and predictive analytics using transformers, GNNs and diffusion models.

Qualifications

• Master’s degree or higher in AI/CS/Mathematics/Materials Science or related fields.
• Solid foundations in physics, thermodynamics and kinetics with AI for Science experience preferred.
• Proficient in Python and deep learning frameworks; strong data analysis skills.

Responsibilities

• AI-driven computations: physics-based simulations and AI-accelerated multiscale modeling.
• AI-enabled R&D: design AI solutions for material discovery, optimization and battery life prediction.
• Integration with AI for Science: connect simulations, experiments and data with ML workflows.
• Data mining & modeling: build predictive models from time-series and experimental data.
• Cutting-edge technology exploration: track large models, agents, RL and digital twins.
• Cross-team adaptation & implementation: collaboration with simulation and engineering teams.

Skills

Python

Machine Learning

GNNs/Transformers

Data Analysis

Cross-team Collaboration

Problem-solving

AI model fine-tuning

Education

Master’s degree or above in AI/CS/Math/Materials Science

Tools

DFT software (VASP, Quantum ESPRESSO)

MD software (LAMMPS, OPENMM)

PyTorch basics

Job description

Focus on core business scenarios including material design, battery R&D, energy storage systems and intelligent manufacturing. Adopt new AI technologies and machine learning algorithms to solve practical industrial problems, and promote the full-integration of AI with renewable energy scientific research, production and simulation, technology implementation, scenario empowerment and efficiency improvement.

Responsibilities

The responsibilities listed cover the full scope of this role. Applicants only need to independently undertake one or two core items, with other tasks completed via teamwork.

• AI-driven Computations: Based on physical view of related material systems of new energy scenarios, and starting from fundamental thermodynamics and kinetics phenomenological equations to complete analysis and simulation; use AI technology to accelerate multiscale materials simulations; complete high-quality physics data generation and close-loop; complete interdisciplinary collaborative verification; complete technical tracking and implementation with related R&D team.
• AI-enabled R&D: Dig deep into core new energy scenarios. Design AI algorithm solutions targeting pain points such as novel material discovery & optimization, synthetic pathway prediction & optimization, process optimization, battery life prediction; complete model development, training, tuning and iterative implementation.
• Integration with AI for Science: Combine AI4S technologies to connect multi-scale simulation, crystal simulation, electrochemical experiments and other scientific research work. Accelerate the R&D iteration of new materials and new battery systems via machine learning, deep learning and generative AI.
• Data Mining & Modeling: Conduct data mining and feature engineering based on time-series data, experimental data and simulation data; build scenario-oriented prediction, classification and optimization models, and deliver implementable algorithm results, patents and technical reports.
• Cutting-edge Technology Exploration: Keep track of cutting-edge applied AI technologies. Explore innovative application scenarios of large models, agents, reinforcement learning and digital twins in the new energy sector, and complete technical verification and project promotion with the R&D team.
• Cross-team Adaptation & Implementation: Cooperate with simulation, experimental and engineering teams to conduct adaptive debugging of algorithms, ensuring stable operation of models in scientific trials and small-scale pilots.

Qualifications

As for the two core proficiency channels listed below (Computational Physics Proficiency & AI Algorithm Proficiency), applicants only need to independently undertake one core item.

• Educational & Academic Background: Master’s degree or above, majoring in Artificial Intelligence, Computer Science, Big Data, Applied Mathematics, Condensed Matter Physics, Computational Physics, Computational Chemistry, Materials Science, New Energy, Automation or related disciplines，possess in-depth understanding of solid-state physics, electrochemical thermodynamics and kinetics. Candidates with practical experience in AI for Science and new energy algorithm implementation are preferred.
• Computational Physics Proficiency: Solid command of principles and application boundaries of density functional theory (DFT) and molecular dynamics simulation (MD), proficient in at least one mainstream DFT software (VASP、Quantum ESPRESSO、CP2K, etc) and MD software (LAMMPS、OPENMM、GPUMD, etc)；skilled in application of machine learning in scientific computations (e.g., machine learning force field) and basic PyTorch; basic knowledge of advanced sampling (enhanced sampling, metadynamics, MC-MD) or coarse-grained modeling；
• AI Algorithm Proficiency: Proficient in Python and mainstream deep learning frameworks (PyTorch/JAX/TensorFlow), with a solid understanding of core machine learning and time-series forecasting algorithms; possess deep R&D experience in advanced architectures such as Graph Neural Networks (GNNs), Equivariant Neural Networks, Transformers, and Diffusion Models, along with a clear understanding of physical constraints (e.g., energy conservation, symmetry) and the applicability boundaries of computational physics methods.
• Practical Modeling Capability: Capable of independent algorithm modeling, model training, parameter tuning and effect iteration. Prior experience in AI projects for battery R&D, energy storage optimization, material design or industrial fault detection is preferred.
• Data Analysis Competence: Solid data analysis capability to process scientific simulation data and industrial time-series data; proficient in the full workflow of data cleansing, feature construction and model verification.
• Collaboration & Problem-solving: Clear logical thinking and strong cross-team collaboration skills. Able to accurately interpret scientific research requirements and deeply integrate AI technologies with new energy R&D scenarios; equipped with robust problem-solving abilities.

Preferred Skills

• Practical experience in large model fine-tuning, industrial agent development and reinforcement learning-based dispatching optimization;
• Familiarity with multi-scale simulation, lithium/perovskite battery material development and electrochemical mechanisms;
• Published papers on AI+Energy or AI+Materials, or possessing relevant technical patents.