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DocuSign, Inc.

Software Engineer - AI Platform

DocuSign, Inc., San Francisco, California, United States, 94199


Company OverviewDocusign 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 collaborative Machine Learning Engineer to join our AI Infrastructure team. Our team is responsible for building Docusign’s centralized platform to create, manage and deploy advanced AI/ML solutions to make customer journeys throughout the Docusign Agreement Cloud more efficient. As a Machine Learning engineer, you will help support all aspects of the machine learning lifecycle, including the research platform, training and deployment pipelines, labeling and serving infrastructure. You will partner with a team of expert applied researchers who specialize in various domains, including document understanding, natural language processing (NLP), computer vision, and more to prototype and productionize solutions for real business use-cases at scale.This position is an individual contributor role reporting to the Senior Manager, Machine Learning.ResponsibilitiesCollaborate with Applied Science, Product and other engineers across multiple offices and time zones to create and deliver new machine learning products and features on time.Contribute to the development of AI infrastructure by building highly scalable gRPC-based services to enable offline and online machine learning pipelines, ensuring enterprise-grade security and reliability.Develop systems to optimize and create AI-assisted data labeling processes while maintaining appropriate and thorough data governance and security standards.Contribute to the development of scalable model training infrastructure to minimize the time it takes to deploy the candidate models to production.Optimize model performance (including open source and licensed LLMs) in production leveraging best practices for CPU/GPU inference on NVIDIA Triton with TensorRT / ONNX.Improve platform observability by implementing tools and required infrastructure to monitor and analyze the performance of deployed AI services.What you bring

BasicMinimum of 5 years of related experience with a Bachelor’s degree; or 3 years of related experience with a Master’s degree; or a PhD without experience; or equivalent experience.Experience building/consuming RESTful and gRPC based web-services.Experience with CI/CD build pipelines, integrated tests, and test-driven development.Familiarity with cloud deployment technologies, such as Kubernetes or Docker containers.Experience with designing and scaling fullstack or distributed backend systems.Experience with Java, Python or similar programming languages.PreferredExperience building machine learning products, data pipelines, or machine learning training and deployment systems.Experience with deployment and monitoring of machine learning models.Ability and desire to move across technology stacks fluently and easily. Experience with LLMs.Familiarity with NLP, Computer Vision domains.Familiarity with Open AI APIs and libraries.Wage Transparency

Pay for this position is based on a number of factors including geographic location and may vary depending on job-related knowledge, skills, and experience. Based on applicable legislation, the below details pay ranges in the following locations:California: $151,800 - $245,150 base salary.Washington and New York (including NYC metro area): $145,800 - $214,350 base salary.This role is also eligible for bonus, equity and

benefits .Life at Docusign

Working hereDocusign is committed to building trust and making the world more agreeable for our employees, customers and the communities in which we live and work. You can count on us to listen, be honest, and try our best to do what’s right, every day. At Docusign, everything is equal.We each have a responsibility to ensure every team member has an equal opportunity to succeed, to be heard, to exchange ideas openly, to build lasting relationships, and to do the work of their life. Best of all, you will be able to feel deep pride in the work you do, because your contribution helps us make the world better than we found it. And for that, you’ll be loved by us, our customers, and the world in which we live.AccommodationDocusign is committed to providing reasonable accommodations for qualified individuals with disabilities in our job application procedures. If you need such an accommodation, or a religious accommodation, during the application process, please contact us at

accommodations@docusign.com .If you experience any issues, concerns, or technical difficulties during the application process please get in touch with our Talent organization at

taops@docusign.com

for assistance.Equal Opportunity Employer

It's important to us that we build a talented team that is as diverse as our customers and where all employees feel a deep sense of belonging and thrive. We encourage great talent who bring a range of perspectives to apply for our open positions. Docusign is an Equal Opportunity Employer and makes hiring decisions based on experience, skill, aptitude and a can-do approach. We will not discriminate based on race, ethnicity, color, age, sex, religion, national origin, ancestry, pregnancy, sexual orientation, gender identity, gender expression, genetic information, physical or mental disability, registered domestic partner status, caregiver status, marital status, veteran or military status, or any other legally protected category.EEO Know Your Rights poster#LI-Hybrid #LI-CF2

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