Amazon is looking for passionate and talented language technologies engineers with background in Machine Translation (MT) and/or Natural Language Processing (NLP) to help us scale our industry-leading MT and NLP technology.
Our mission is to enable superior experience for all of Amazon’s customers in their native languages by developing and deploying state-of-the art technology and applications in Machine Translation (MT), Natural Language Processing (NLP) and Machine Learning (ML).
As part of our MT development team, you will work alongside our MT research team on developing, deploying, maintaining and supporting our platform fleet of Machine Translation systems that serve our various Amazon teams and clients. Your work will directly impact millions of our customers in the form of products and services that make use of our MT and NLP technology. You will gain hands-on experience with Amazon’s heterogeneous language data sources and large-scale computing resources to accelerate advances in MT and NLP.
Train and evaluate statistical and neural models for MT using Amazon’s proprietary MT technology.
Analyze the accuracy and performance of MT models and systems.
Assist in deploying, maintaining, and supporting Amazon’s platform fleet of dedicated MT systems in production for our clients.
Design and implement software to collect, analyze and prepare large natural language data sets.
Collaborate with Amazon MT research team in experimentation, integration, and testing of technology improvements.
BS in Computer Science, Computational Linguistics, Language Technologies, Linguistics, or in another data engineering field.
Experience working on data-intensive Natural Language Processing technology systems or similar systems.
Good implementation skills in Java (or other high-level programming language) as well as Python or similar scripting languages.
Exceptional analytical, troubleshooting and problem solving skills.
Formal or informal knowledge of linguistics and/or foreign languages.
How to apply:
Please mention NLP People as a source when applying
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