AI Algorithm Expert
发布于： 16/07/2026
Shanghai,Beijing East China
Permanent
生命科学、医疗保健、制药
Main responsibility:
Design and develop cutting edge algorithms to solve the challenges in drug discovery such as making undruggable targets druggable.
Optimize generative model architectures to ensure its viability in real practice.
Contribute to the research and development of novel deep learning architecture, training paradigms (e.g. generative, multi-modal) and algorithms etc. tailored to specific tasks.
Maximize model performance, scalability and robustness for production use on fast-paced drug discovery projects.
Enhance the interpretation of model predictions to improve collaboration efficiency for rational design.
Collaborate cross functionally with computational chemists, biologists, data scientists, medicinal chemists, and other researchers to incorporate domain knowledge and right data into model development.
Stay informed on the state of art AI research.
Basic Qualifications
PhD in computer science, machine learning, mathematics, or related disciplines. Or Master with outstanding experience in advanced ML algorithm development
Proven expertise in modern machine learning: deep understanding of neural network architecture and hands-on experience developing novel models or algorithms
Strong skills in key AI domains: experience with deep generative models, graph neutral networks, or reinforcement learning etc.
Proficient programing and math skills: expert in python and deep learning frameworks (e.g PyTorch, Tensorflow) with solid engineering practices. Firm grasp of linear algebra, probability, etc., and algorithm underling machine learning.
Strong verbal and written communication skills and ability to work independently and cooperatively.
Preferred Qualifications
Research excellence demonstrated through first-author publications in top-tier ML conferences (e.g. NeurIPS, ICML, ICLR) or high impact journals, or key contributions to open-source ML projects
Proven expertise in modern machine learning: deep understanding of neural network architecture and hands-on experience developing novel models or algorithms
Experience with large-scale data and models: working with big biological datasets or training large models (e.g. LLMs or other foundation models); familiarity with distributed training, cloud computing, and tools for handling massive datasets
Design and develop cutting edge algorithms to solve the challenges in drug discovery such as making undruggable targets druggable.
Optimize generative model architectures to ensure its viability in real practice.
Contribute to the research and development of novel deep learning architecture, training paradigms (e.g. generative, multi-modal) and algorithms etc. tailored to specific tasks.
Maximize model performance, scalability and robustness for production use on fast-paced drug discovery projects.
Enhance the interpretation of model predictions to improve collaboration efficiency for rational design.
Collaborate cross functionally with computational chemists, biologists, data scientists, medicinal chemists, and other researchers to incorporate domain knowledge and right data into model development.
Stay informed on the state of art AI research.
Basic Qualifications
PhD in computer science, machine learning, mathematics, or related disciplines. Or Master with outstanding experience in advanced ML algorithm development
Proven expertise in modern machine learning: deep understanding of neural network architecture and hands-on experience developing novel models or algorithms
Strong skills in key AI domains: experience with deep generative models, graph neutral networks, or reinforcement learning etc.
Proficient programing and math skills: expert in python and deep learning frameworks (e.g PyTorch, Tensorflow) with solid engineering practices. Firm grasp of linear algebra, probability, etc., and algorithm underling machine learning.
Strong verbal and written communication skills and ability to work independently and cooperatively.
Preferred Qualifications
Research excellence demonstrated through first-author publications in top-tier ML conferences (e.g. NeurIPS, ICML, ICLR) or high impact journals, or key contributions to open-source ML projects
Proven expertise in modern machine learning: deep understanding of neural network architecture and hands-on experience developing novel models or algorithms
Experience with large-scale data and models: working with big biological datasets or training large models (e.g. LLMs or other foundation models); familiarity with distributed training, cloud computing, and tools for handling massive datasets