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Applied Scientist Core Machine Learning Intern


Cambridge, United Kingdom


Amazon is building a European Machine Learning Team in Cambridge and is seeking a Machine Learning Scientist Intern to join the group!

For Amazon, machine learning is a keystone technology to (1) recommend physical products (e.g. books and fashion) as well as digital (e.g. music and films) (2) recognize spoken language and answer questions through "Alexa", Amazon's digital assistant (3) translate reviews, (4) forecast demand for products and so much more.

The Cambridge site is already a key innovation hub for Amazon and the Machine Learning Team is located alongside the Evi Team that develops Alexa's knowledge base, and a Prime Air Team, that is helping to develop Amazon's drone delivery system.

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 development of innovative machine learning algorithms for the modeling and analysis of data. The candidate will be expected to work in research areas such as probabilistic modeling, scalable inference methods and latent variable models. Challenges may involve dealing with very large data sets and requirements on throughputs.


· Current enrollment in a degree-granting college or university working towards a PhD in Machine Learning, Computer Vision, Statistics, Applied Mathematics, or a related field.
· Hands on experience in predictive modeling and analysis, in particular one or more of: probabilistic modeling, surrogate modeling optimization, unsupervised feature learning, scalable machine learning, deep learning.
· At least one refereed academic publications in these areas.
· Good coding skills. Experience in Python is a plus.
· Good communication skills and the ability of 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 Bayesian methods (e. g. Gaussian processes).
· Hands on experience in MXNet.


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