Senior Machine Learning Engineer - AI Platform

Company Overview

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 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.

Responsibilities

• Collaborate 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.

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).

What You Bring

• Minimum 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.

Wage Transparency

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.