Senior Product Manager, Applied AI

Metropolis•Los Angeles, CA

13h•$180,000 - $205,000•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. The role focuses entirely on internal AI initiatives, partnering with leaders in Finance, Operations, Go-To-Market, and other areas to identify high-impact bottlenecks and reduce process friction. The successful candidate brings strong analytical and product leadership, working across operational workflows and modern AI architecture while applying product rigor through clear PRDs, disciplined roadmaps, and structured discovery. They will work closely with AI Engineers and vendors to turn operational challenges into scalable agentic workflows, internal applications, and automation systems with measurable business impact. The position prioritizes the internal Applied AI roadmap, translates business challenges into engineering specifications, and drives adoption of internal technologies. It involves designing and deploying internal applications, prompt pipelines, RAG implementations, and agentic workflows; defining requirements and measurable success metrics; leading iterative development based on user feedback; overseeing change management; and monitoring reliability, adoption, and business value. The role also expects hands-on prototyping. Metropolis follows an office-first model, requiring corporate team members to work onsite at least four days per week. The anticipated base salary is $180,000 to $205,000 annually. Compensation may also include healthcare benefits, a 401(k) plan, disability coverage, basic life insurance, stock options, and bonus plans.

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.
A proven record of driving internal process transformations, operational optimizations, or software tools for internal business stakeholders.
Practical understanding of 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.
Exceptional 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 usage.
Experience operating in hyper-growth startups or environments managing rapid organizational change.

Responsibilities

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