Software Engineer

What youll 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)
• Design and implement agentic frameworks to automate complex workflows and enhance system intelligence, leveraging LLMs and agent-based architectures
• 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)

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)
• 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

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 …