11 Feb

Research Engineer Jobs Vacancy in Messagepoint Toronto

Position
Research Engineer
Company
Messagepoint
Location
Toronto ON
Opening
11 Feb, 2018 1 day ago

Messagepoint Toronto urgently required following position for Research Engineer. Please read this job advertisement carefully before apply. There are some qualifications, experience and skills requirement that the employers require. Does your career history fit these requirements? Ensure you understand the role you are applying for and that it is suited to your skills and qualifications.

Follow the online directions, complete all the necessary fields, and provide all relevant information so your application is submitted correctly. When you click the 'Apply this Job' button (open in new window) you will be taken to the online application form. Here you will be asked to provide personal and contact details, respond to employment-related questions, and show how you meet the key selection criteria.

Research Engineer Jobs Vacancy in Messagepoint Toronto Jobs Details:

About Us

Messagepoint is a leader in developing and delivering innovative software and services within the Customer Communications Management (CCM) market. The Messagepoint solution is the 2016 SIIA CODiE winner for the Best Multi-channel Publishing Platform. Messagepoint helps companies strengthen their customer communications by enabling business users to control the entire messaging lifecycle for all print or digital communications without burdening IT.

Summary

The Messagepoint Rationalizer team offers a unique opportunity to innovate in the Enterprise Customer Communication Management (CCM) space using an artificial intelligent (AI) enabled data driven approach for content management. We apply cutting edge natural language processing (NLP) and machine learning (ML) techniques towards creating a holistic scalable enterprise application development platform, supporting advanced use cases focused on document ingestion, parsing, content disambiguation & creation, and semantic analysis of multi-model multi-lingual customer communication content.

We are currently seeking a software engineering role, that must possess an in-depth knowledge of the core AI technology stack and tools required for implementing the use cases outlined above. A proven industry experience of using the related AI toolkits and libraries is absolutely mandatory.

Although the role is software engineering focused, the potential candidate must also have an understanding of the related core AI concepts (e.g. classification, clustering, topic modelling, language modelling, part-of-speech tagging, semantic analysis) for text based content analysis and enrichments. In addition, experience in deploying and managing cloud based AI solutions is nice to have. Familiarity with Linked Data, and ontology based data modelling is also nice to have.

The successful candidate will work closely with the Rationalizer research team translating their work into cloud-deployable software solutions. In addition, the successful candidate will play an integral part in the architecture, design and development of the Rationalizer components of the Messagepoint platform.

Responsibilities

Design and develop a distributed compute platform for content analysis.
Translate Machine Learning (ML) and Natural Language Processing (NLP) models into scalable components.

Qualifications and Skills

Software Development Environments
Strong industry experience in either Java, Scala, or Python
Software content management tools such as git (preferred) or SVN.
Experience with functional programming.
Software Engineering
Deep understanding of core software design patterns
Practical experience with distributed computing;
Apache Spark is preferred, Apache Hadoop Map/Reduce or experience with other similar distributed compute engines.
Automated code deployment to various environments.
Machine Learning
Industry experience with any of the following machine learning toolkits
Weka, Spark, h20.ai, Tensorflow, Keras, R.
Natural Language Processing
Industry experience with any of the following toolkits:
Gate NLP, Open NLP, Stanford NLP, Python NLTK, Apache UIMA, R
Linked Data
Exposure to any of the following linked data initiatives
RDF, DBPedia, WikiData, GeoNames
Graph Databases
Industry experience with any of the following graph database engines:
JanusGraph (or Titan Graph), Cassandra, HBase, HIVE.
Must have working experience with TinkerPop graph stack.
Gremlin and/or SPARQL.

We’re looking forward to hearing from you!


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