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 As a Machine Learning Engineer on the AI Applications team, you will design and build the AI Features that power Docusign’s next generation of intelligent systems and user experience. You will bridge the gap between core AI research and production-grade engineering, developing scalable systems for autonomous agents, advanced retrieval systems, and automated model optimization with the goal of improving the outcomes of our users. This position is an individual contributor role reporting to the Senior Manager, Machine Learning Engineering. Responsibility Build and maintain high-performance distributed systems to support agreement processing, understanding or creation Design frameworks for multi-agent systems, focusing on state management, reliability, and long-running autonomous workflows Architect sophisticated Retrieval-Augmented Generation (RAG) pipelines and advanced context management strategies to improve model accuracy and relevance Design, implement, and own comprehensive evaluation frameworks, including the construction of domain-specific evaluation sets, golden datasets, and running structured offline/online experiments to maintain a strict quality bar Author, version, and optimize production prompts, ensuring high semantic accuracy and robust defenses against prompt injection Own and execute end-to-end model fine-tuning processes to enhance feature alignment for tailored agreement use cases Implement robust ML pipelines and CI/CD workflows, focusing on observability, telemetry instrumentation, log parsing, and the seamless deployment of generative AI services Collaborate cross-functionally with Applied Science and Product Management teams via a joint triage framework to deliver AI capabilities into production-grade features 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 5+ years of experience in machine learning engineering, software engineering, or related operational roles Candidates must demonstrate proficiency in Python design patterns, asynchronous programming, and performance optimization Proven experience deploying and managing containerized ML services using Kubernetes (k8s) Experience in building high-performance, scalable distributed systems that can support large-scale agreement processing and real-time user experience An understanding of the full lifecycle is required, specifically managing data ingestion, model training, and production-grade monitoring Direct experience building with Large Language Models (LLMs), specifically implementing complex prompt engineering Experience deploying and maintaining ML models in high-traffic, production environments Preferred Cloud Native development experience with Azure services Experience designing "agent-loop" architectures that involve tool-use, self-correction, and multi-step reasoning Ability to design and own comprehensive evaluation systems, including the creation of "golden datasets" and domain-specific evaluation sets Experience with distributed task queues or stateful workflow engines for managing complex, multi-step AI processes 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 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. Applicant and Candidate Privacy Notice #LI-Hybrid #LI-VP3
Machine Learning Engineer
DocuSign · Enterprise · Bangalore, India · Engineering
Machine Learning Engineer on the AI Applications team responsible for designing and building AI Features for Docusign's intelligent systems. The role involves bridging AI research and production engineering, developing scalable systems for autonomous agents, advanced retrieval systems, and automated model optimization. Key responsibilities include building distributed systems, designing multi-agent frameworks, architecting RAG pipelines, developing evaluation frameworks, optimizing prompts, fine-tuning models, and implementing ML pipelines with a focus on generative AI services.
What you'd actually do
- Build and maintain high-performance distributed systems to support agreement processing, understanding or creation
- Design frameworks for multi-agent systems, focusing on state management, reliability, and long-running autonomous workflows
- Architect sophisticated Retrieval-Augmented Generation (RAG) pipelines and advanced context management strategies to improve model accuracy and relevance
- Design, implement, and own comprehensive evaluation frameworks, including the construction of domain-specific evaluation sets, golden datasets, and running structured offline/online experiments to maintain a strict quality bar
- Author, version, and optimize production prompts, ensuring high semantic accuracy and robust defenses against prompt injection
Skills
Required
- Python design patterns
- asynchronous programming
- performance optimization
- Kubernetes (k8s)
- Large Language Models (LLMs)
- prompt engineering
- MLOps
- distributed systems
Nice to have
- Azure services
- agent-loop architectures
- tool-use
- self-correction
- multi-step reasoning
- distributed task queues
- stateful workflow engines
What the JD emphasized
- 5+ years of experience in machine learning engineering, software engineering, or related operational roles
- Proven experience deploying and managing containerized ML services using Kubernetes (k8s)
- Experience in building high-performance, scalable distributed systems that can support large-scale agreement processing and real-time user experience
- Direct experience building with Large Language Models (LLMs), specifically implementing complex prompt engineering
- Experience deploying and maintaining ML models in high-traffic, production environments
Other signals
- design and build AI Features
- bridge the gap between core AI research and production-grade engineering
- developing scalable systems for autonomous agents
- advanced retrieval systems
- automated model optimization
- improving the outcomes of our users