Lead Machine Learning Engineer

Company Overview

Docusign brings agreements to life. More than 1.8 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 the Docusign AI-native IAM 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 Machine Learning engineer to help build industry-leading state of the art AI/ML solutions. You will be able to get your hands on all aspects of the AI /ML feature life cycle by leveraging your expertise in computer vision, NLP, and document understanding. You will be building production-level machine learning models that deliver more personalized and automated customer experiences throughout the Docusign Agreement Platform.

This position is an individual contributor role reporting to the Director of Docusign.

Responsibility

• Work together with your team to perform model development, research, testing, and evaluation of existing and emerging deep learning methods and technologies that can be effectively applied to the contract domain

• Apply the latest architectures and technologies to build Docusign IP and solve complex NLP challenges including, but not limited to, generating representations, text understanding, semantic retrieval, contextual extractions, summarization

• Apply computer vision for document understanding tasks such as layout and object detection, image classification and tagging

• Develop an understanding of the technologies, methods and the architecture within Docusign product development

• Understand, assist, and improve the existing model training, evaluation, and online inferencing processes, define online metrics, and design user feedback for our AI / ML features

• Work closely with the engineering partners to deploy models into production, build scalable AI systems, monitor and improve performance metrics

• Work closely with Product Management to translate user scenarios and product requirements into designs and plans for robust, customer-agnostic machine learning solutions

Job Designation

Hybrid: Employee divides their time between in-office and remote work. Access to an office location is required. (Frequency: Minimum 2 days per week; may vary by team but will be weekly in-office expectation)