The Amazon Machine Learning team in Cambridge develops innovative machine learning methods for the modelling and analysis of complex data. We are embedded in Amazon's Supply Chain Optimization Technologies team. The particular research areas of the group are uncertainty quantification, data-efficient learning, streaming applications and deep learning. We focus on the mathematical and computational challenges that arise in these topics.
We are recruiting a curious and creative software development engineer 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 and 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 BSc in Computer Science, or a related field.
- Strong software development skills.
- Good communication skills and the ability to working in a team.
- Ability to convey rigorous computational concepts and considerations to non-experts.
- Hands on experience in machine learning.