算法工程师（机器学习方向）(J23629)

1. Develop and optimize machine learning (ML) algorithms including major Deep Learning models, e.g. NLP DL models (Seq2seq, transformers, etc.), diffusion models, graph neural networks to solve real-world spectrum analysis and protein design problems in pharmaceutical industry
2. Integrate domain knowledge and ML techniques to build models to analyze complex chemical spectra data or protein interaction data
3. Digest up-to-date state-of-the-art (SOTA) ML algorithms, and develop systematic experiment pipelines to evaluate and reproduce SOTA results
4. Generate technical documentation, help and drive junior project members to deliver expected technical results
5. Work with other teams and departments to transfer ML prototypes into systems and platforms

• 任职资格：

1、985/211 (or equivalent overseas) graduates; Master degree/PhD degree in computer science or related areas (mathematics, computational biology, etc)

2、Requirement: Associate Director: 3-year+ experience in AI/ML, Director: 4-year+ in AI/ML with Chem AI hands-on experience; Senior Director: 5-year+ in AI/ML plus track records in AI and Chem AI research & development

3、Master/PhD in STEM with strong hands-on technical and industrial experience in ML and AI projects; expertise in NLP deep learning models and protein generative models is preferred

4、Excellent problem formulation and problem solving skills with outstanding communication and teamwork capability, good sense of data and outstanding research capability;

5、Outstanding programming skills using Python (additional experience in SQL/Java/C/C++ is a plus);

6、Strong hands-on technical skills in implementing Deep Learning (DL) algorithms such as Transformers, CNN, LSTM, GNN, diffusion models with DL frameworks (Pytorch/Tensorflow), experience in developing customized algorithms to improve outcomes in realistic applications

7、Strong research capability in digesting and reproducing up-to-date ML/AI algorithms and results

8、Knowledge and experience in pharmaceutical and clinical domains is preferred; experience in spectrum analysis, bioinformatics and other related areas is a strong plus