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Applied Scientist Core Machine Learning Intern


Cambridge, United Kingdom


The Core Amazon Machine Learning team in Cambridge develops innovative machine learning methods for the modelling and analysis of complex data. We collaborate closely with other science, engineering and product teams in Amazon in several application domains such as robotics, natural language understanding and many more. The particular research areas of the group are uncertainty quantification, data-efficient learning, streaming applications and privacy aware and deep learning. We focus on the mathematical and computational challenges that arise in these topics.

We are recruiting a curious and creative machine learning scientist intern who is prepared to learn new skills and who is willing to collaborate with scientists and engineers to implement new machine learning methods.

The internship will involve working on the deployment of novel inference methods for probabilistic machine learning algorithms. The candidate will be expected to work in research areas such as Bayesian machine learning, deep neural networks, and analysis of streaming/time-series data. Challenges will involve designing and implementing scalable algorithms that can meet the constraints arising in production environments. Motivated candidates will have an opportunity to apply their academic knowledge to industry-scale problems and get firsthand experience on the development of approximate inference methods for probabilisitic models.


· Current enrolment in a degree-granting college or university working towards a PhD in Machine Learning, Data Mining, Statistics, Applied Mathematics, or a related field.
· Hands on experience in predictive modelling and analysis, in particular one or more of: Bayesian machine learning, probabilistic programming, streaming data analysis.
· At least one refereed academic publication in these areas.
· Good coding skills. Experience in Python is a plus.
· Good communication skills and the ability to working in a team.


· Ability to convey rigorous mathematical concepts and considerations to non-experts.
· Ability to distill problem definitions, models, and constraints from informal business requirements; and to deal with ambiguity and competing objectives.
· Strong software development skills.
· Familiarity with one or more of: approximate inference (variational, GANs, implicit models).


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