Product Manager Intern

Docusign•San Francisco, CA

6h•Hybrid

About The Position

Docusign’s Product Management organization builds systems and experiences that power its go-to-market strategy and sales motion. The Product Manager Intern will help define features, streamline workflows, and explore AI-powered capabilities to make GTM and sales systems more scalable and intelligent. The intern will work with senior product managers, GTM Operations, engineers, designers, and data and AI specialists to gather requirements, shape the roadmap, support launches, and measure business outcomes. This is a three-month internship and an individual-contributor role reporting to the Sr. Manager, Business Applications. The position is hybrid, with at least two days per week in an office; the weekly in-office expectation may vary by team.

Requirements

Currently pursuing a bachelor's or master's degree in Computer Science, Artificial Intelligence, Engineering, Business, or a related field.
Expected graduation in December 2027 or May/June 2028.
Experience with the Software Development Lifecycle (SDLC) and Product Management Lifecycle.
Experience with tools such as Lucid, JIRA, reports and dashboards, and Confluence.
Experience with Google Suite.

Nice To Haves

Strong analytical and problem-solving skills, including the ability to break down complex processes.
Self-starter mindset with strong organizational skills and attention to detail.
Excellent written and verbal communication skills, with comfort conducting interviews and presenting to stakeholders.

Responsibilities

Conduct user research to understand pain points and opportunities in GTM and sales workflows.
Select and optimize technical tools and potential AI solutions to improve productivity and solve complex problems, fostering a culture of learning.
Document product requirements and translate business needs into clear user stories and acceptance criteria.
Support product roadmap planning and prioritization in partnership with the applications team.
Identify areas for improvement and AI-driven automation.
Apply conceptual understanding of AI or ML and systems thinking to orchestrate AI microservices across core enterprise platforms, using data reasoning and context engineering to guide intelligent analysis and agile delivery.
Use a conceptual understanding of supervised and unsupervised learning, large language models, RAG architecture, and embeddings; deep coding is not required, but architectural understanding is essential.
Collaborate with application and engineering teams to build AI prototypes.
Contribute to requirements workshops and feedback sessions, capturing inputs and aligning on expected outcomes.
Collect, organize, and analyze product usage and business metrics, such as adoption, operational efficiency, and customer satisfaction.
Partner with design and engineering teams to clarify requirements and keep stakeholders aligned on status and timelines.