Machine Learning Engineer

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).

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 product ionize solutions for real business use-cases at scale.

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

Responsibility

• Write clean, maintainable, and well-tested code for AI infrastructure services and data pipelines under the guidance of senior engineers

• Assist in building and maintaining CI/CD pipelines and workflows that automate model training and deployment

• Help monitor the performance of deployed AI services by implementing logging, metrics, and alerting (observability) to ensure system reliability

• Partner with Applied Scientists and Product Managers to understand requirements and help operationalize ML models for document understanding and NLP tasks

• Author unit and integration tests for your code components to ensure high quality and stability of the AI platform

• Develop scripts and small tools to support data labeling processes and ensure data governance standards are met

• Learn about new ML technologies (LLMs, Triton Inference Server, Kubernetes) and engineering best practices through code reviews and mentorship actively

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)

Positions at Docusign are assigned a job designation of either In Office, Hybrid or Remote and are specific to the role/job. Preferred job designations are not guaranteed when changing positions within Docusign. Docusign reserves the right to change a position's job designation depending on business needs and as permitted by local law.

Basic

• Bachelor’s degree in Computer Science, or a related technical field (or equivalent experience)

• Experience with internships or applicable academic projects

• Experience with at least one major programming language, preferably Python or C#

• Experience with data structures, algorithms, and software engineering fundamentals

• Experience with version control systems (e.g., git) and writing unit tests

• Experience with RESTful APIs and how web services work

Preferred

• Previous internship experience in software engineering, data engineering, or machine learning

• Coursework or academic projects related to Machine Learning, Natural Language Processing (NLP), or Computer Vision

• Familiarity with containerization technologies like Docker or Kubernetes

• Exposure to cloud platforms (AWS, Azure, or GCP)

• Interest in Large Language Models (LLMs) and Generative AI

• Experience with SQL or data processing libraries (e.g., Pandas, Num Py)

Pay for this position is based on a number of factors including geographic location and may vary depending on job-related knowledge, skills,…