AI Engineer for Energy Materials & Battery R&D

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

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.