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Applied Machine Learning Scientist - Natural Language

Applied Machine Learning Scientist - Natural Language

Job ID 
Posted Date 
Amazon Corporate LLC
Position Category 
Research Science
Recruiting Team 

Job Description

We are looking for experienced, passionate, and talented Applied Machine Learning Scientist to build and iterate Natural Language models that will facilitate conversation with users in this exciting new domain, and will improve the quality of life for millions of people. In this position, you will work in a multi-disciplinary team of scientists, engineers, strategic partners, product managers and subject domain experts. You will have an impact on all the steps of the Natural Language Understanding pipeline: from minute details of audio acquisition through affecting the way Automatic Speech Recognition (ASR) models are created all the way to training your own Natural Language Understanding (NLU) and designing dialog interaction flows.

Basic Qualifications

* MS or Ph.D. in Machine Learning or closely related area or equivalent work experience
* Solid background in statistical learning techniques for NLP (HMMs, CRFs, SVMs, LDA, LSI, MRFs, etc.)
* Experience implementing ML/NLP algorithms as well as the ability to modify standard algorithms (e.g. changing objectives, working out the math, implementing and scaling)
* Experience developing prototypes by manipulating and analyzing complex, high-volume, high-dimensionality data from varying sources
* Experience with Object Oriented design and development using any of programming languages Java, Python, Scala, C++
* Facility with UNIX
* Excellent communication skills

Preferred Qualifications

· Experience with deep learning
· Experience with sequential data modelling, time-series analysis and multi-modal learning algorithms
· Track-record of novel algorithm development, e.g. publications in one or more of the following: KDD, WWW, NIPS, NAACL, ACL, SIGIR, EMNLP, ICML etc
· Experience with large scale data analysis tools such as Spark, Hadoop etc.
· Experience with filesystems, server architectures, and distributed systems