Senior Researcher/Senior Research SDE (AI)

We are on the cusp of a new frontier in which machine learning and artificial intelligence are transforming scientific discovery. We seek to drive major advances in sciences with machine learning, with a focus on ‘’ scenarios. Through these advances, we aim to empower real-world impact on some of the most pressing problems facing society including climate change, green energy, sustainable materials, and the discovery of new drugs.  
 
AI for Science is a global team in Microsoft Research focusing on the opportunity to transform scientific modelling and discovery through large-scale deep learning.  We aim to advance this frontier and to drive real-world impact at a global scale. The AI for Science team encompasses multiple disciplines across machine learning, engineering, and the natural sciences and spans several geographical sites in Europe, Asia, and the US.  
 
The field of machine learning has evolved significantly in recent years, with many of the most impactful contributions coming from larger teams of people collaborating closely on well-defined and challenging goals.  Furthermore, AI for Science in particular requires a combination of machine learning, engineering, and natural sciences, which again emphasizes the importance of collaboration and teamwork. 
 
We are seeking highly motivated and experienced researchers/engineers with expertise in machine learning. 

Qualified candidates should have:

• A strong background in deep learning, with publications in top ML conferences, e.g., NeurIPS, ICLR, ICML.  
  
• Master degree or above.
• Passion in science related problems, including but not limited to, drug discovery, biology, and materials science. 
  
• Willingness to join a large team, work on a big project, and generate big impact through collective efforts and team collaboration. 
  
• Proficiency in Python and relevant ML libraries (e.g., PyTorch). 
  
• Experience with transformer-based models (e.g.,  large language models (LLMs) like GPT, Llama), or expertise in reinforcement learning or generative models (e.g., diffusion models, flow-matching).  
  
• Applicants should be fluent in both spoken and written English. They must demonstrate their ability to conduct solid research in machine learning, as evidenced by high-quality publications or a proven track record of innovation. While a background in biology, drug, or materials is advantageous, it is not mandatory. Candidates must be able to collaborate effectively with other researchers, and we are particularly interested in those who can work across disciplinary boundaries. 

#Research # AI for Science

What we need are your passion and dedication in machine learning for science, and your commitment to doing world-class research.