Senior Product Manager, Applied AI

Metropolis•Seattle, WA

2d•$170,000 - $200,000•Onsite

About The Position

Metropolis is seeking a Senior Product Manager, Applied AI to lead internal product strategy and drive an AI-first operational transformation across the company. This role focuses entirely on internal AI initiatives and partners with leaders across Finance, Operations, Go-To-Market, and other areas to identify high-impact bottlenecks and reduce process friction. The successful candidate will bring strong analytical and product leadership skills, working across complex operational workflows and modern AI architectures. They will apply structured product practices—including PRDs, roadmaps, and discovery—while moving quickly through AI-first and vendor-supported approaches when appropriate. Working alongside AI Engineers and vendors, the Senior Product Manager will translate operational challenges into scalable agentic workflows, internal applications, and automation systems with measurable business impact. The role includes defining and prioritizing the internal Applied AI roadmap, specifying solutions and success measures, leading iterative development and adoption, and monitoring reliability and business outcomes. The position requires being on-site at least four days per week under Metropolis’s office-first model. The anticipated base salary is $170,000 to $200,000 USD annually. Total compensation may also include healthcare benefits, a 401(k) plan, short- and long-term disability coverage, basic life insurance, stock options, bonus plans, and other benefits.

Requirements

At least 6 years of product management or technical product leadership experience, including at least 2 years focused on shipping AI-powered applications, LLM implementations, or process automation systems.
Proven experience driving internal process transformations, operational optimizations, or building software tools for internal business stakeholders.
Practical understanding of modern AI capabilities, including LLMs, prompt systems, agentic patterns such as tool calling and multi-step reasoning, and RAG architectures.
Hands-on fluency building with AI tools, including prompt engineering, AI coding agents, and rapid prototyping.
Working familiarity with modern data architectures, data pipelines, and cloud infrastructure, including SQL, Snowflake, and AWS.
Excellent communication and stakeholder management skills, including the ability to explain complex technical concepts to non-technical business leaders.
Strong change-management instincts and user empathy to help non-technical teams transition to AI-first workflows.
Entrepreneurial drive and a bias toward shipping quickly, testing iteratively, and solving open-ended problems in a fast-paced environment.

Nice To Haves

Experience managing third-party vendor selection, evaluation, and integration for enterprise AI or software solutions.
Experience scaling internal productivity tools or AI agents within high-growth technology companies.
A Computer Science, Data Science, or related technical background.
Direct familiarity with enterprise security, compliance, and governance frameworks for enterprise AI use.
Experience operating in hyper-growth startups or environments managing rapid organizational change.

Responsibilities

Prioritize the internal Applied AI product roadmap and translate ambiguous business challenges into prioritized engineering specifications and AI-first solutions.
Partner with business leaders across Finance, Operations, and Go-To-Market to map workflows, identify automation opportunities, and drive adoption of internal technology.
Collaborate with AI Engineers to design, scope, and deploy internal applications, prompt pipelines, RAG implementations, and agentic workflows.
Define requirements, PRDs, and measurable success metrics, tracking outcomes such as time savings, error reduction, and cost impact.
Lead agile product development cycles, gather feedback from internal users, and iteratively improve deployed AI tools and output quality.
Oversee change management to support adoption of internal tools and systems across business units.
Monitor production reliability, internal tool adoption, and the business value delivered by deployed systems.
Prototype solutions directly rather than limiting work to writing specifications for others.