Director, Product Management, AI

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).

This role is focused on accelerating AI product development across Docusign by providing essential services and frameworks, while also building and owning core foundational AI features like deep contract understanding. You will lead efforts to enable other teams to build AI features and occasionally co-develop initial product versions, ensuring efficient and scalable AI solutions.

The ideal candidate will have extensive product management experience, a deep understanding of the AI development lifecycle, and a proven ability to ship AI products or platforms s role also involves optimizing AI services for efficiency and contributing to AI governance.

This position is a people manager role reporting to GVP, B2B Product Management.

Responsibility

• Convert from PDF-to-text with layout and table understanding

• Capture standard agreement structure, including key types, fields, and clauses

• Provide a secure, compliant, fully anonymized, and auditable datastore that teams can pull from to get training data

• Own capabilities for end-users to build and refine custom document understanding models

• Own services and tools to accelerate document understanding tasks, e.g. snippet selection

• Accelerate AI-driven product development across Docusign by providing services, frameworks, and guidelines for established and new scenarios

• Co-develop the initial version of the product with a feature focused team, but more frequently provide teams training, access, and support to use existing services

• Develop, maintain, and improve infrastructure AI services and frameworks so that each team can take advantage of common scenarios at scale, across geographies, and in a secure, data compliant environment (e.g. capture embeddings, vectorize, search, RAG, etc.) which includes tooling for labeling, data set curation, and evaluation

• Optimize AI services for scale, cost, and latency

• Invest in key capabilities to maximize scale while minimizing cost and latency, e.g. fine-tuning smaller more efficient models, routing workloads to the right models, and more

• Set the AI governance and compliance standard for Docusign

• Set the gold standard for how sensitive customer data is handled for the whole company, and provide the necessary training, communication, brand guidelines, core services, and review process to continue to deliver on Docusign’s trust promise to our customers

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.

Basic

• 12+ years experience in building software, ideally at scale, and for enterprises
• Experience defining, launching, and scaling demonstrably successful products
• Experience managing teams of 5-10 PMs, with managers reporting to you
• Experience supporting multiple product lines or initiatives simultaneously, while thoughtfully building services to support them
• Ability to navigate and drive collaboration within a complex organizational structure involving not just engineering, and data science teams, but also field teams that need product education for…