Product Manager, Enterprise Intelligence

Glean•Mountain View, CA

5h•$170,000 - $250,000•Hybrid

About The Position

Glean is seeking a strategic, execution-oriented Product Manager to help define and build its emerging Enterprise Intelligence product area. The role sits at the intersection of AI, enterprise workflows, analytics, and product strategy, shaping products that turn Glean’s enterprise context layer into proactive intelligence for leaders and teams. The aim is to help organizations understand what is changing, where value is being created or lost, and where to act. Working with the Product lead and partners across engineering, design, data, go-to-market, and customer-facing teams, this person will identify promising product opportunities, bring structure to an ambiguous space, and translate enterprise intelligence concepts into concrete product experiences. The product team is lean, so this role will have significant influence over the roadmap and the team’s development. The position calls for both strategic thinking and granular product implementation, including specific interfaces and workflows. The role is hybrid, with four days per week in Glean’s San Francisco office. The standard base salary range is $170,000–$250,000 annually. Compensation may also include variable compensation, equity, and benefits, depending on the role and other factors. Benefits include medical, vision, and dental coverage, a generous time-off policy, a 401(k) plan, a home office improvement stipend, annual education and wellness stipends, regular company events, and daily healthy lunches.

Requirements

At least 5 years of product management experience.
A track record of owning and shipping meaningful product areas in B2B SaaS, enterprise software, AI products, analytics products, or workflow/intelligence platforms.
Ability to translate complex, ambiguous problems into clear product experiences and move between strategy and detailed execution.
Strong interest in AI and enterprise software.
Technical fluency and ability to partner with engineering and data teams on systems involving structured and unstructured data, knowledge graphs, retrieval, or intelligence layers.
Ability to demonstrate detailed product thinking about workflows, interaction models, and sequencing capabilities for customer adoption.
Comfort operating in ambiguous environments and creating clarity, momentum, and alignment.
Excellent written and verbal communication skills.
Proven ownership, initiative, and results delivery.
Ability to collaborate effectively with cross-functional partners.
Learning and growth mindset.
Mission-first orientation, with an understanding that success is measured by product and team success.
Early adoption of AI in personal product work and enthusiasm for helping customers accelerate AI adoption.

Responsibilities

Define and drive the Enterprise Intelligence product strategy and roadmap, translating company vision into product bets, experiences, and execution plans informed by customer needs and market signals.
Engage with customers, prospects, and internal stakeholders to understand high-value enterprise intelligence use cases, particularly needs for proactive contextual insights.
Build workflows, dashboards, recommendations, and proactive experiences that turn Glean’s enterprise context layer, graph, and connected system intelligence into actionable information for teams and leaders.
Partner with engineering, design, and data teams to translate ambiguous concepts into well-scoped products with clear user flows, interaction patterns, quality standards, and measurable outcomes.
Prioritize and execute with a customer focus, balancing long-term platform opportunities, urgent user pain points, strategic customer requests, and company priorities.
Collaborate with go-to-market, solutions, support, and forward-deployed teams to learn from customer problems and reflect enterprise realities in product strategy.
Establish product foundations for Enterprise Intelligence at Glean, including proactive insights, organizational visibility, process understanding, and cross-functional decision support.
Specify how product opportunities should work in practice, including concrete workflows, UI behavior, and adoption paths.