Applied AI Engineer - Hong Kong

CATL; Hong Kong; On-site; HKD 900,000 - 1,500,000; Full time

Job summary

CATL is seeking a high-level AI scientist to advance AI-powered materials research and battery R&D in Hong Kong. You will design AI-driven computations, enable AI-based R&D, and integrate AI with science workflows across simulations and experiments.

The role requires deep expertise in computational physics, ML architectures, and cross-disciplinary collaboration to push energy storage innovations forward.

Qualifications

• Master’s degree or above in AI, CS, math, physics or related fields; experience in AI for science preferred.
• Solid command of DFT/MD, at least one mainstream DFT/MD tool, and ML in scientific computations.
• Proficient in Python and major DL frameworks; experience with advanced architectures and physical constraints.
• Ability to independently model, train, tune and iterate AI models for energy materials.
• Strong data analysis for simulation and time-series data; cross-team collaboration skills.
• Relevant battery R&D, energy storage or materials design experience preferred.

Responsibilities

• AI-driven computations: multiscale material simulations and AI-accelerated analysis.
• AI-enabled R&D: develop AI solutions for material discovery and process optimization.
• Integration with AI for Science: connect simulations with experiments and data.
• Data mining & modeling: build predictive and optimization models from diverse data.
• Cutting-edge technology exploration: track and verify new AI tech in energy.
• Cross-team adaptation: collaborate with simulation, experiment and engineering teams.

Skills

Computational Physics

AI Algorithm Proficiency

Python & PyTorch

ML for Science

Data Analysis

Cross-team Collaboration

Battery R&D Experience

Education

Master’s degree or above in AI, CS, math, physics or related fields

Tools

VASP

Quantum ESPRESSO

CP2K

LAMMPS

OPENMM

GPUMD

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.