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’re looking for passionate and talented Software Engineers to build out the machine learning platform and infrastructure that will power Docusign’s IAM. You will help support all aspects of the machine learning lifecycle, specifically areas like data storage, annotation, training systems, and telemetry infrastructure. Our team is responsible for building a platform that supports a team of machine learning engineers and data scientists and can serve AI solutions to Docusign at scale. This role will be a part of the AI platform team at Docusign, which builds analytics products that utilize AI, machine learning, and cutting-edge deep learning based models to provide insights for the Docusign IAM. Our team is distributed across the San Francisco/Bay Area, Seattle, Ireland and India and you will be working collaboratively across these teams. This position is an individual contributor role reporting to the Senior Manager, Software Engineering - AI Platform. Responsibility Design, implement, and maintain backend and platform services that power Docusign’s AI and Insight capabilities for Intelligent Agreement Management (IAM) Build and enhance components across the machine learning lifecycle, including data ingestion and storage, feature/label pipelines, annotation tooling, training workflows, and model serving infrastructure Develop reliable, secure, and scalable RESTful services and APIs that expose AI-powered insights and analytics to internal product teams and customer-facing applications Contribute to service observability by adding metrics, logging, dashboards, and alerts while participating in incident triage and root cause analysis to improve system reliability Apply cutting-edge AI tools to write clean, well-tested code, participating in code reviews, following team engineering best practices for design, testing, and deployment, and continuously adapting to new technologies Collaborate closely with machine learning engineers, data scientists, and other backend teams to translate requirements into robust platform and product features Work as part of a distributed team across time zones, communicating clearly and documenting decisions to ensure alignment and smooth handoffs Learn and adopt new technologies continuously in AI platforms, data systems, and backend engineering to improve performance, reliability, and developer experience Demonstrate strong curiosity about emerging AI capabilities and industry trends, proactively exploring how they can be applied to our platform and products Challenge the status quo by questioning existing designs and processes and proposing innovative, AI-driven solutions that improve developer experience and customer value Look for opportunities to apply AI thoughtfully in every phase of development—from design and implementation to testing, deployment, and operations—to deliver better outcomes for our customers 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 professional software engineering experience building backend or platform services in a production environment (or equivalent internship/industry experience) Owning design and implementation of services and independently executing it Proficiency in at least one modern programming language used for backend development (for example, Java, C#, Go, or TypeScript/Node.js) and familiarity with object-oriented and/or functional design principles Experience developing and debugging services that interact with databases or data stores (SQL or NoSQL) and external APIs Experience with unit, integration, and end-to-end testing, and with using version control and modern CI/CD pipelines Commitment to the service observability lifecycle by joining on-call rotations and serving as the primary lead for service health, ensuring rapid mitigation and long-term resolution of production issues Understanding of core distributed systems concepts such as scalability, reliability, latency, and fault tolerance Preferred Experience building or supporting components of an AI or machine learning platform (for example, data pipelines, feature stores, training orchestration, model serving, or experimentation frameworks) Experience designing and operating cloud-native services (for example, Kubernetes, Docker, serverless functions, or managed data and messaging services) Familiarity with observability tools and practices (metrics, logs, traces, and dashboards) and with on-call or incident response processes Experience working with large-scale data processing frameworks or streaming systems (such as Spark, Flink, Kafka, or similar) Exposure to machine learning concepts and workflows, and comfort partnering with ML engineers and data scientists to productionize models Experience with secure coding practices and building services that comply with privacy, security, and compliance standards Prior experience in SaaS or enterprise platforms, especially analytics or developer platforms Strong problem-solving skills, a willingness to dive into unfamiliar areas across the stack, and a learning mindset Effective communication and collaboration skills, with the ability to work with cross-functional partners in a distributed team 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 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
Software Engineer
DocuSign · Enterprise · Bangalore, India · Engineering
Software Engineer to build out the machine learning platform and infrastructure powering Docusign's Intelligent Agreement Management (IAM). Responsibilities include supporting the ML lifecycle (data storage, annotation, training systems, telemetry), building a platform for ML engineers and data scientists, and serving AI solutions at scale. The role involves designing, implementing, and maintaining backend and platform services for AI capabilities, developing components across the ML lifecycle, and contributing to service observability.
What you'd actually do
- Design, implement, and maintain backend and platform services that power Docusign’s AI and Insight capabilities for Intelligent Agreement Management (IAM)
- Build and enhance components across the machine learning lifecycle, including data ingestion and storage, feature/label pipelines, annotation tooling, training workflows, and model serving infrastructure
- Develop reliable, secure, and scalable RESTful services and APIs that expose AI-powered insights and analytics to internal product teams and customer-facing applications
- Contribute to service observability by adding metrics, logging, dashboards, and alerts while participating in incident triage and root cause analysis to improve system reliability
- Apply cutting-edge AI tools to write clean, well-tested code, participating in code reviews, following team engineering best practices for design, testing, and deployment, and continuously adapting to new technologies
Skills
Required
- 5+ years of professional software engineering experience building backend or platform services in a production environment
- Proficiency in at least one modern programming language used for backend development (for example, Java, C#, Go, or TypeScript/Node.js)
- Familiarity with object-oriented and/or functional design principles
- Experience developing and debugging services that interact with databases or data stores (SQL or NoSQL) and external APIs
- Experience with unit, integration, and end-to-end testing
- Experience with using version control and modern CI/CD pipelines
- Commitment to the service observability lifecycle by joining on-call rotations and serving as the primary lead for service health
- Understanding of core distributed systems concepts such as scalability, reliability, latency, and fault tolerance
Nice to have
- Experience building or supporting components of an AI or machine learning platform (for example, data pipelines, feature stores, training orchestration, model serving, or experimentation frameworks)
- Experience designing and operating cloud-native services (for example, Kubernetes, Docker, serverless functions, or managed data and messaging services)
- Familiarity with observability tools and practices (metrics, logs, traces, and dashboards) and with on-call or incident response processes
- Experience working with large-scale data processing frameworks or streaming systems (such as Spark, Flink, Kafka, or similar)
- Exposure to machine learning concepts and workflows, and comfort partnering with ML engineers and data scientists to productionize models
- Experience with secure coding practices and building services
What the JD emphasized
- 5+ years of professional software engineering experience building backend or platform services in a production environment
- Experience building or supporting components of an AI or machine learning platform
- Experience designing and operating cloud-native services
- Experience working with large-scale data processing frameworks or streaming systems
- Exposure to machine learning concepts and workflows
Other signals
- machine learning platform
- infrastructure
- data storage
- annotation
- training systems
- telemetry infrastructure
- model serving infrastructure