Docusign brings agreements to life. Over 1.5 million customers and more than a billion people in over 180 countries use Docusign solutions to accelerate the process of doing business and simplify people’s lives. With intelligent agreement management, Docusign unleashes business-critical data that is trapped inside of documents. Until now, these were disconnected from business systems of record, costing businesses time, money, and opportunity. Using Docusign’s Intelligent Agreement Management platform, companies can create, commit, and manage agreements with solutions created by the #1 company in e-signature and contract lifecycle management (CLM).

What you'll do

Docusign is looking for a passionate, talented, and inventive MLE to help build industry-leading state of the art solutions. You will be leveraging text mining, deep learning, content understanding, document processing and more. You will prototype machine learning models that deliver more personalized and automated customer experiences throughout the Docusign Agreement Cloud.

This position is an individual contributor role reporting to the Director, Machine Learning.

Responsibility

• Work together with the ML team to perform research, testing and evaluation of existing and emerging NLP/ML/DL methods and technologies that could be effectively applied to contractual/legal data
• Be knowledgeable in and be able to apply NLP techniques in order to maintain and extend the current rule-based, supervised and unsupervised methods
• Be knowledgeable in and able to apply ML/DL algorithms and technologies to NLP tasks such as Named Entity Recognition, POS tagging, Parsing, Sentiment Analysis, Clustering, text prediction etc.
• Develop an understanding of the technologies, methods and the architecture within Docusign product development. Also an understanding of the entire software development end-to-end process as well as the product code and release management process will be essential
• Understand, assist and improve the existing training maintenance and enrichment process
• Work within the Machine Learning team to deploy models to production, using existing and emerging machine learning methods and technologies
• Work closely with Product Management to translate product requirements into robust, customer-agnostic machine learning success metrics
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