Generative AI Engineer (Generative AI for Chemistry & Materials)

Youwill work end-to-end along the core loop: Data → Generation → Evaluation →Iteration:

1. 01

   Data & Representations： Integrate public datasets and internal data sources (experiments, computations, literature). Perform cleaning, standardization, deduplication, and data quality assessment. Build high-quality datasets for training and benchmarking.

2. 02

   Generative Model Development Design, train, and optimize generative models (e.g., Transformers/GPT, Diffusion, Flow Matching). Support structure generation and optimization for chemistry/materials objects.

3. 03

   Multi-objective & Constrained Generation Translate multiple property targets and physical/chemical constraints into computable conditions. Enable controllable generation and trade-off optimization across multiple metrics.

4. 04

   Evaluation & Decision Layer Build reliable evaluation and decision modules (scoring, filtering, re-ranking, uncertainty estimation). Improve validity, robustness, and reproducibility of generated results.

5. 05

   Engineering & Cross-functional Delivery Collaborate with domain experts and experimental/simulation teams. Build reproducible pipelines (training/inference/evaluation/monitoring). Deliver actionable candidates and milestone reports to support closed-loop iteration.

1. 01

   M.S. degree or above (Ph.D. preferred) in CS/ML/computational chemistry/materials science or related fields.

2. 02

   Strong deep learning fundamentals; proficiency with PyTorch/TensorFlow; ability to write clean, modular, maintainable research/production code.

3. 03

   Hands-on experience with at least one class of generative models: Transformer/GPT, Diffusion, or Flow Matching (training, tuning, inference, evaluation).

4. 04

   Strong data/system skills: able to independently build data pipelines, manage experiments, design metrics, and perform error analysis.

5. 05

   Strong cross-disciplinary collaboration skills: able to abstract domain constraints into algorithmic problems and drive implementation. Preferred Qualifications：

6. 01

   Applied experience in AI for Science (chemistry/materials/biology/physics).

7. 02

   Familiarity with RDKit, molecular/materials structure toolchains, GNNs, and/or reinforcement learning.

8. 03

   Experience with distributed training, inference acceleration, and industrial-grade tuning/experiment tracking (e.g., Weights & Biases, MLflow, Optuna). Experience integrating models into real R&D workflows (service deployment, reliability, monitoring, regression testing).