Product Manager 3, Internal AI

MongoDB•San Francisco, CA

42d•Hybrid

About The Position

We are seeking a highly motivated IT Product Manager for Internal AI to help deliver and scale AI-powered solutions across our enterprise IT landscape. You’ll support the product lifecycle for internal AI tools; partnering with senior PMs, engineers, and operational teams to turn defined opportunities into shipped capabilities that improve service delivery, knowledge management, automation, and decision support. This is a hands-on role for someone early in their product career who is excited to learn, work cross-functionally, and make measurable impact. We are looking to speak to candidates who are based in San Francisco, CA or Palo Alto, CA for our hybrid working model.

Requirements

3–5 years of experience in product management or adjacent roles in IT, data, or software (internships, apprenticeships, research projects, or relevant full-time experience all count).
Familiarity with AI/ML concepts and workflows (LLMs, agents, copilots, prompt design, evaluation) and an eagerness to learn how to apply them to internal enterprise use cases.
Strong fundamentals in PM craft, including writing concise PRDs/requirements, defining acceptance criteria, running lightweight discovery, and partnering with engineering.
Clear communication and collaboration skills with the ability to translate technical details into user value and to coordinate across multiple stakeholders.
Comfort with data and measurement (basic experimentation concepts, funnels, dashboards) to track adoption and outcomes.
Awareness of responsible AI practices (data privacy, security, model limitations, bias) and willingness to follow established guardrails.
Bachelor’s degree in Computer Science, Information Systems, Data/Analytics, or related field, or equivalent practical experience.

Nice To Haves

Exposure to enterprise IT systems and how internal tools integrate with them (e.g., SuccessFactors, Salesforce).
Hands-on familiarity with AI platforms (e.g., OpenAI, Anthropic, Azure AI, AWS AI, Google Cloud AI) or agentic frameworks in coursework or projects.
Basic scripting or SQL to explore data, validate assumptions, or support instrumentation.

Responsibilities

Product execution and delivery Support the end-to-end lifecycle for internal AI products; from discovery tasks, writing clear requirements, coordinating prototypes, to assisting with launches and post-launch iteration under guidance from senior PMs.
Backlog and roadmap support Maintain clean user stories, acceptance criteria, and prioritization for a scoped backlog; contribute to release notes and lightweight roadmap updates.
User research and requirements Participate in interviews, help map critical user journeys, synthesize insights, and translate them into problem statements and MVP scopes.
Adoption and enablement Assist with UAT, documentation, training materials, and rollout plans that drive adoption and measurable outcomes (time saved, CSAT, deflection).
Analytics and outcomes Partner with engineering/analytics to define success metrics, instrument basic telemetry, and report on feature usage and impact.
Stakeholder collaboration Work with IT partners (Applications, Infrastructure, Security, Service Desk) and business stakeholders to clarify needs, gather feedback, and keep work unblocked.
Governance and responsibility Learn and apply responsible AI practices and guardrails in partnership with data, security, and compliance teams.
AI evangelism Contribute to internal share-outs, demos, and office hours to build awareness of what’s shipped and what’s next.