Software 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).

As a Software Engineer on the AI Platform team, you will architect and build the robust distributed systems and backend infrastructure that power global AI capabilities. This is a systems-first role where you will be responsible for the "pipes and engines" of our AI operations, ensuring that document processing, model serving, and data orchestration are reliable, resilient, and horizontally scalable. You will bridge the gap between core AI research and production-grade engineering, developing scalable platforms for autonomous agents, advanced retrieval systems, and automated model optimization. This position is an individual contributor role reporting to the Director, Machine Learning Engineering Responsibility Build and maintain high-performance distributed systems to support large-scale model inference and data processing Design and build resilient, horizontally scalable backend services capable of handling high-throughput data processing and model interaction Develop the core infrastructure for model serving and inference runtimes, focusing on maximizing resource utilization and minimizing latency Architect resilient data ingestion and processing pipelines that handle massive datasets while ensuring data integrity, multi-tenant isolation, and high availability Optimize system performance, identify bottlenecks, and implement advanced monitoring (SLOs, SLAs) to ensure high reliability for mission-critical AI services 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 software engineering experience with a primary focus on distributed systems and scalable backend architecture Experience building, deploying, and maintaining ML models in high-traffic, production environments Experience with Python and professional experience with at least one other strongly-typed language (e.g., Java, Go, or C#) Experience with container orchestration (e.g., Kubernetes), messaging systems (e.g., Kafka, Service Bus), and high-performance database design Experience building production systems that operate at massive scale with strict uptime and latency requirements Bachelor’s or Master’s degree in Computer Science or a related technical field Preferred Experience with stateful workflow engines or distributed task queues (e.g., Temporal) for managing complex, multi-step AI processes Familiarity with frameworks designed for horizontal scaling of compute-intensive ML workloads (e.g., Ray, Spark) Expertise in Azure or GCP infrastructure, specifically around identity, security, and compliant networking (VNETs, PEPs) Experience building platform-level services for LLM orchestration, RAG architectures, and specialized prompt configuration layers Wage Transparency Pay for this position is based on a number of factors including geographic