Postdoctoral Machine Learning Scientist - Amazon - Cambridge - Wizbii

Postdoctoral Machine Learning Scientist

  • By Amazon
  • Cambridge (United Kingdom)
  • Information systems

Job description


Postdoctoral Machine Learning Scientist

Location: Cambridge
Compensation: Competitive Salary, Amazon restricted stock units and benefits payments
Posting Date: 30th May 2018
Closing Date: 27th June 2018

Amazon is building a European Machine Learning Team in Cambridge and is seeking a Machine Learning Scientist 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 who is willing to collaborate with scientists and engineers to research and develop new machine learning methods.

The scientist 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.

Seeked profile


· Undergraduate degree in computer science, software engineering or undergraduate degree in numerical discipline (e.g. physics, maths, engineering).
· PhD in machine learning
· Recent record of publication in internationally-leading machine learning venues (e.g. ICML, AISTATS, UAI, JMLR, TPAMI).
· Experience in Python.
· Good communication skills and the ability of working in a team.
· Hands on experience in predictive modelling and analysis
· Expert in approximate inference for Deep Gaussian Processes

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