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Open nowPosted 48 days ago

Senior Machine Learning Engineer

DocuSign266 open roles

Where
Bengaluru, India
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Your applicationOpen nowSenior Machine Learning EngineerDocuSign · Bengaluru, India

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The clock on this job

Early applications get read.

8.4% of postings close within 7 days. Measured by our own scanner across the market. DocuSign postings stay open a median of 38 days.

Share of postings closed within
  1. 1.8%1 day
  2. 4.0%3 days
  3. 8.4%7 days
  4. 15.4%14 days
  5. 34.1%30 days
This job: posted 48 days ago

DocuSign median: 38 days open

The posting

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 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. Responsibility 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 for technical domains (code analysis, log parsing) Experience with distributed data processing and streaming technologies (Apache Spark, Kafka, Flink) Experience with software engineering fundamentals (Python, C++, or Go), CI/CD for ML, and experience deploying models via APIs (FastAPI, Triton Inference Server) Experience building/consuming RESTful and gRPC based web-services Experience with cloud deployment technologies, such as Kubernetes or Docker containers Experience with designing and scaling fullstack or distributed backend systems Preferred Experience with Elastic Search technology Experience with microservice architecture Experience shipping highly available, scalable service on Azure Experience with the entire software development lifecycle, including version control (git) build process, testing, and code release Ability to work in a dynamic, fast-moving environment Life at Docusign Working here Docusign 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. Accommodation Docusign 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 [email protected]. If you experience any issues, concerns, or technical difficulties during the application process please get in touch with our Talent organization at [email protected] for assistance. Applicant and Candidate Privacy Notice #LI-Hybrid #LI-VP3

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. Responsibility 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

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 for technical domains (code analysis, log parsing) Experience with distributed data processing and streaming technologies (Apache Spark, Kafka, Flink) Experience with software engineering fundamentals (Python, C++, or Go), CI/CD for ML, and experience deploying models via APIs (FastAPI, Triton Inference Server) Experience building/consuming RESTful and gRPC based web-services Experience with cloud deployment technologies, such as Kubernetes or Docker containers Experience with designing and scaling fullstack or distributed backend systems Preferred Experience with Elastic Search technology Experience with microservice architecture Experience shipping highly available, scalable service on Azure Experience with the entire software development lifecycle, including version control (git) build process, testing, and code release Ability to work in a dynamic, fast-moving environment

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