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Principal Al/Machine Learning Scientist, Drug Discovery (China)

TandemAI

TandemAI

Software Engineering, Data Science
United States
Posted on Mar 16, 2026
About TandemAI

TandemAI is building a next generation artificial intelligence platform to accelerate scientific discovery and drug development. Our mission is to transform how therapeutics are discovered by integrating advanced machine learning approached and physics-based simulations.

Role Overview

TandemAI is seeking an experienced Principal AI/Machine Learning Scientist to join our research teams located in Shanghai and Suzhou, China. This role will focus on developing advanced machine learning models to accelerate drug discovery and molecular design.

The ideal candidate will bring deep expertise in machine learning research with significant academic and/or industrial experience and the ability to translate scientific insights into scalable AI systems. You will work closely with interdisciplinary teams across software engineering, biology, and chemistry to build models that address complex scientific challenges.

This position is well suited for researchers who are passionate about designing and applying AI/ML to real world biomedical problems and advancing the frontier of AI driven drug discovery.

Key Responsibilities

• Design, develop, and optimize novel machine learning architectures for physics, chemistry, and drug discovery

• Develop algorithms for molecular design, protein modeling, and biological data analysis

• Apply machine learning techniques to large biological and chemical datasets to generate predictive insights.

• Develop models capable of reasoning about molecular and biological systems.

• Build benchmark datasets and validation pipelines in collaboration with domain experts.

• Analyze experimental results and iterate on model architectures and training strategies.

• Translate research ideas into scalable tools and systems that support drug discovery workflows.

• Contribute to scientific publications, internal research reports, and technical presentations.

• Build scalable machine learning pipelines and distributed training systems.

• Write efficient and maintainable code to support experimentation and deployment.

• Work with engineering teams to integrate models into production platforms.