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Salary Research Internships at Microsoft provide a dynamic environment for research careers with a network of world-class research labs led by globally-recognized scientists and engineers. Our researchers and engineers pursue innovation in various scientific and technical disciplines to help solve complex challenges in diverse fields.
We are looking for highly motivated interns to work with us to develop next generation of generative AI models that can be deployed on our in-house developed hardware.
Relevant model categories are diffusion models, energy-based models, Hopfield networks and deep-equilibrium models.
The successful intern will do research into the strengths of such models or into how such models can be deployed on our hardware including a systems analysis of the accelerator.
There is no closing deadline for this post. The post will be filled once suitable candidates are found so if you are interested please apply as soon as possible. When submitting your application, include your CV with a list of publications as an attachment. You may also include your published work. For more information about the post, please feel free to email msrcmlinternship@microsoft.com.
“Interns are expected to be physically located in UK for the duration of their internship.”
Responsibilities
- Design, implementation, and experimental validation of machine learning models and Algorithms.
- Collaboration with other researchers and engineers as part of a project team.
- Clearly communicating research ideas and results in writing, such as research papers, presentations, or research notes for internal and external audiences.
Qualifications
Required Qualifications
- Must be accepted or currently enrolled in a PhD program in CS, EE or a related STEM field.
- Understanding and demonstrated research track record of machine learning such as deep learning, deep generative models, causal machine learning, reinforcement learning through active research in a related PhD program or publications in academic conferences (NeurIPS, ICML, ICLR), journals, or workshops. .
- Demonstrated ability to write code, programming abilities in Python, comfortable with using Git and GitHub processes such as code reviews, and software testing principles.
- Experience in building machine learning systems with PyTorch.
