Staff Product Manager - Internal AI

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Adoption assistance: Not verified
• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Childcare support

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Learning budget

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Salary

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401(k) match

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Market context

Median wage (BLS OEWS)

$102,662 national median

Projected growth (BLS Employment Projections)

+5.4% - Faster than average

137% above the BLS national median for product management aggregate.

Matched to SOC 11-1021 - Product Management aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

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Deadline

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Where they hire

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About this role

Staff Product Manager - Internal AI Palo Alto We are seeking a highly skilled Staff IT Product Manager for Internal AI to drive the strategy, delivery, and adoption of AI-powered solutions across our enterprise IT landscape. This role will be pivotal in shaping how AI transforms our internal operations, from service delivery and knowledge management to automation and decision support. The ideal candidate has a proven track record of managing enterprise-scale AI/ML products, collaborating across IT and business functions, and delivering 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. Key Responsibilities - AI Product Strategy & Roadmap - Define and execute the internal AI product strategy aligned with enterprise IT and business goals - Own multiple product lines in the internal AI product space - Prioritize high-value use cases across IT functions (helpdesk, infrastructure, security, applications, enterprise data) - Balance quick wins (AI copilots) with longer-term bold initiatives (AI-driven automation and decision-making) - Experience in implementing AI solutions for enterprises our size and scale. This should include enabling agentic platforms for organizations and bringing to the table the best practices, pitfalls and learnings from such experiences - Product Management Execution - Embrace the product mindset and own the lifecycle of AI products-from ideation, critical user journey definition, requirements gathering, vendor evaluation, prototyping, and implementation to scaling in production - Define and manage product backlogs, roadmaps, and success metrics - Drive adoption and ensure

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