What you'll doAs a Machine Learning Engineer on the AI Platform team, you will design and build the foundational infrastructure that powers Docusign's next generation of intelligent systems. 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 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
- Develop platform-level tools for automated prompt engineering, evaluation, and optimization to accelerate the AI development lifecycle
- Implement robust ML pipelines, focusing on observability, versioning, and the seamless deployment of generative AI services
Job DesignationHybrid: 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 bringBasic- 5+ years of experience in machine learning engineering, software engineering, or related operational roles
- Experience in software engineering with a focus on distributed systems and scalable backend architecture
- Deep understanding of the ML lifecycle, from data ingestion and training to production monitoring
- Experience building with LLMs, including RAG architectures and sophisticated prompt engineering
- Experience deploying and maintaining ML models in high-traffic, production environments
- Expertise in Python and experience with modern ML frameworks such as PyTorch
Preferred- Experience with distributed task queues or stateful workflow engines for managing complex, multi-step AI processes
- Experience with frameworks designed for horizontal scaling of compute-intensive ML workloads
- Experience designing "agent-loop" architectures that involve tool-use, self-correction, and multi-step reasoning
- Familiarity with vector storage systems and high-throughput data processing pipelines
Wage TransparencyPay 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: $164,700.00 - $266,000.00 base salary
Washington: $158,300.00 - $232,575.00 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
