The Microsoft Research AI Frontiers group in Redmond is looking for a highly-qualified Machine Learning Research Engineer to build state-of-the-art tools for evaluating and understanding foundation models, with a focus of real-world uses of Artificial Intelligence (AI) and Machine Learning (ML). Our team conducts influential research published at top-tier venues in AI and ML (including NeurIPS, ICML, ICLR, CVPR, and FAccT) and works within Microsoft’s Responsible AI ecosystem to impact our AI-driven technologies such as Azure, Office, and Bing. 

We’re seeking an exceptional Machine Learning Research Engineer with demonstrated ability for technical work in the space of large foundational models with proficient coding and machine learning skills. This person is:

• Passionate about rigorous evaluation, understanding, and development of foundational models.  
• Motivated to make successful research methods accessible to the larger AI community through prototypes, open-source libraries, and development tools.  
• Proficient in design thinking and Object-Oriented Design, building clean, modular, maintainable and user-friendly open-source ML frameworks. 
• Experienced in measuring and maximizing the impact of open-source libraries. 

As a Machine Learning Research Engineer, you will play a crucial role in designing and developing impactful, high quality and well-engineered frameworks to empower the scientific evaluation, understanding, and development of foundational models. You will work closely with a team of passionate researchers and engineers to make sure such frameworks are compatible with modern cloud platforms, ML frameworks and libraries, model architectures, and various data modalities. You will also play a central role in defining and running large-scale experiments that contribute to our team’s research. 

We are looking for team players who are interested in developing next-generation platforms and tools for Machine Learning as well as conducting state-of-the-art research.Topics of interest include but are not limited to: rigorous evaluation and benchmarking, advances in AI interpretability, bias and fairness, and safety in real-world deployments. Our group takes a holistic approach to studying foundational models that includes a variety of data modalities (language, vision, multi-modal, and structured data) and modern model architectures. Candidates should demonstrate expertise in many of these aspects or show that they are interested in generalizing their skills into a variety of modalities and architectures.

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