The Core Amazon Machine Learning team in Cambridge develops innovative machine learning methods for the modeling 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 privacy-aware machine learning algorithms. The candidate will be expected to work in research areas such as differential privacy, Gaussian processes, Bayesian machine learning, or computational statistics. 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 privacy-aware methods for data analysis.