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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 ‘fifth paradigm’ 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 new 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 sites in Europe.

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 emphasises the importance of collaboration and teamwork.

We are seeking a highly motivated and experienced Sr RSDE with expertise in machine learning and distributed systems. The ideal candidate will have a deep understanding of machine learning and be proficient in the design, planning, and implementation of tools and technology to support AI-driven scientific research.

Required:

• Master's degree or equivalent work experience in Computer Science, Physics, Engineering, Chemistry, Mathematics or a related field.
  
• Strong familiarity with Linux and the open-source ecosystem.
  
• Proficient experience working with machine learning and large datasets.
  
• In-depth understanding of open source machine learning frameworks (e.g., PyTorch, ggml, llama.cpp, vllm).
  
• Experience building complex systems on the cloud.
  
• Experience building and optimizing distributed systems and large-data applications, including those using tensor accelerators or GPUs.
  
• Strong analytical, problem-solving, and communication skills.
  
• Passionate about pushing the boundaries of science. Prior experience developing high-performance scientific software is not required, but preferred.

#Research #AI for Science

• Architect, design, and implement scalable and robust solutions for machine learning and scientific research involving large volumes of heterogeneous data.
• Build and optimize distributed data processing and model building pipelines.
• Develop and maintain tools and technologies for building, training, optimizing, scaling machine learning solutions.
• Collaborate with cross-functional teams, including scientists, researchers, and software engineers.
• Document and share best practices across the organization.
• Maintain the highest standards in code quality and software design.