Job description


  • Entry level
  • No Education
  • Salary to negotiate
  • Cambridge


The 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 curious and creative machine learning scientist interns who are prepared to learn new skills and who are willing to collaborate with scientists and engineers to implement new machine learning methods.

The internship will involve working on the deployment of innovative machine learning algorithms for the modelling and analysis of data. The candidate will be expected to work in research areas such as probabilistic modelling, surrogate model optimization, uncertainty quantification or probabilistic numerics. Specific challenges will be dealing with very large datasets and designing algorithms able return uncertainties in their calculations.


- Current enrollment 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, probabilistic modelling, surrogate modelling optimization, unsupervised feature learning, scalable machine learning, probabilistic numerics
- 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)

  • software