Senior 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).

We are looking for a Senior Machine Learning Engineer to join our Search team. You will be responsible to help turn the Docusign Agreement AI engine Iris into the institutional brain of our customers' agreement ecosystems. You will work on designing and building the core ML and LLM systems that transform standalone agreements into a unified, queryable knowledge graph. In parallel, you will help architect Iris's long-term memory (LTM) layer so the platform can remember prior chats, user decisions, and corrective feedback across sessions, closing the loop between our assistant and workspace experiences. If you are excited about building knowledge-graph-centric retrieval systems and evolving context layers that continuously learn from real user interactions, this role will let you shape the future of how Docusign customers reason over their entire agreement portfolio. This position is an individual contributor role reporting to the Sr. Manager, Software Engineering.

Design and implement the core knowledge graph and schema that model agreements, addenda, amendments, and related entities as explicit nodes and edges, enabling multi-hop and hierarchical reasoning over complex contract portfolios Build LLM Wiki style compilation pipelines that transform newly ingested agreements into structured, interconnected markdown or graph-based knowledge bases, including concept, entity, and comparison pages that stay in sync with upstream document changes Develop and productionize retrieval architectures that leverage the compiled knowledge base for high-precision, multi-step question answering and navigation across corporate hierarchies Architect and implement Iris's long-term memory (LTM) layer, including storage, retrieval, and summarization mechanisms that persist chat history, user actions in Canvas, and administrative overrides as episodic memories over time Create ML and LLM components that fuse contract-centric retrieval (GraphRAG + LLM Wiki) with institutional memory (LTM) so Iris can adapt behavior based on prior decisions, exemptions, and corrections without requiring users to restate context Own the end-to-end lifecycle of these models and systems-from feature and schema design through training, evaluation, deployment, and monitoring-with a focus on reliability, safety, and debuggability in production environments 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) 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. What you bring Basic 8+ years of professional experience in Machine Learning Engineering or Data Science, with a strong background in Software Engineering Experience with PyTorch or TensorFlow, specifically regarding Time Series analysis (forecasting/anomaly detection) and NLP Experience building applications using LLMS (RAG pipelines, LangChain, vector databases) specifically