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Software Engineer

Salary$140k – $235k

LocationUS-SFO-Main, United States

Work modehybrid

Typefull-time

DepartmentEngineering

EquityIncluded

Company size10,000+ people

First seen3mo ago

Last seen3d ago

About the company

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 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 location and may vary depending on job-related knowledge, skills, and experience. Based on applicable legislation, the below details pay ranges in the following locations: California: $146,400.00 - $235,375.00 base salary Washington: $140,100.00 - $206,775.00 base salary This role is also eligible for the following: Bonus: Sales personnel are eligible for variable incentive pay dependent on their achievement of pre-established sales goals. Non-Sales roles are eligible for a company bonus plan, which is calculated as a percentage of eligible wages and dependent on company performance. Stock: This role is eligible to receive Restricted Stock Units (RSUs). Global benefits provide options for the following: Paid Time Off: earned time off, as well as paid company holidays based on region Paid Parental Leave: take up to six months off with your child after birth, adoption or foster care placement Full Health Benefits Plans: options for 100% employer paid and minimum employee contribution health plans from day one of employment Retirement Plans: select retirement and pension programs with potential for employer contributions Learning and Development: options for coaching, online courses and education reimbursements Compassionate Care Leave: paid time off following the loss of a loved one and other life-changing events 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 States Not Eligible for Employment This position is not eligible for employment in the following states: Alaska, Hawaii, Maine, Mississippi, North Dakota, South Dakota, Vermont, West Virginia and Wyoming. Equal Opportunity Employer It’s important to us that we build a talented team that is as diverse as our customers and where all employees feel a deep sense of belonging and thrive. We encourage great talent who bring a range of perspectives to apply for our open positions. Docusign is an Equal Opportunity Employer and makes hiring decisions based on experience, skill, aptitude and a can-do approach. We will not discriminate based on race, ethnicity, color, age, sex, religion, national origin, ancestry, pregnancy, sexual orientation, gender identity, gender expression, genetic information, physical or mental disability, registered domestic partner status, caregiver status, marital status, veteran or military status, or any other legally protected category. EEO Know Your Rights poster #LI-Hybrid

Requirements

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

About the role

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

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