Principal Product Manager, AI Platform

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Principal Product Manager, AI Platform based in the United States. Overview Join an AI-focused organization transforming marketing services by redesigning core business operations around an intelligent, proprietary AI platform. As a Principal Product Manager, AI Platform, you will identify complex operational challenges and turn them into scalable, data-driven product solutions. You will work hands-on with real-world data, AI coding tools, and engineering teams to rapidly prototype ideas and bring validated solutions into production. Your work will shape intelligent decision engines, performance measurement capabilities, and closed-loop attribution systems that connect customer actions to business outcomes. You will help establish measurable frameworks for operational improvements while ensuring AI-driven systems remain reliable, practical, and adaptable to different client needs. Working within a small, collaborative product team, you will have significant ownership over product direction, technical problem-solving, and platform evolution. This remote opportunity is ideal for a technically fluent product leader who combines strong systems thinking with experimentation, analytical rigor, and a bias for action.

Identify high-impact problems: Investigate complex operational challenges, define clear problem statements, establish measurable success criteria, and prioritize opportunities where AI can deliver meaningful business value. Prototype AI-driven solutions: Use AI coding tools and real-world data to rapidly develop functional prototypes, test hypotheses, and validate potential solutions before committing to full-scale development. Drive production implementation: Partner closely with engineering teams to transform successful prototypes into reliable, maintainable, and scalable production systems. Develop intelligent decision engines: Lead the design and development of decision-making capabilities tailored to different user needs, business contexts, and operational requirements. Improve operational measurement: Develop frameworks to measure and address challenges such as content decay, using data and actionable metrics to identify opportunities for continuous improvement. Build closed-loop attribution: Guide the development of attribution capabilities that connect client activities and decisions to measurable revenue outcomes, supporting better performance evaluation and optimization. Shape platform architecture and capabilities: Define a platform strategy that balances reusable, shared capabilities with the specialized requirements of different client segments and industries. Apply systems thinking: Evaluate downstream effects, operational dependencies, scalability considerations, and potential failure modes when designing and generalizing product solutions. Ensure AI reliability: Anticipate AI limitations, edge cases, and failure scenarios, incorporating appropriate safeguards and validation methods into production-ready systems. Collaborate across disciplines: Work directly with engineers and other stakeholders to align on requirements, communicate product decisions, and maintain clear, concise documentation. Engage with users and data: Gather direct user insights, analyze operational data, and continuously refine product direction based on evidence, user needs, and measurable results. Requirements Relevant product experience: Approximately 2–11 years of experience building products or technology solutions, with a track record of taking ideas from initial concept through implementation. Hands-on prototyping skills: Demonstrated experience building and shipping functional prototypes using AI coding tools and real-world data to test assumptions and validate solutions. AI systems knowledge: Strong understanding of AI capabilities, limitations, failure modes, and edge cases, including the ability to design solutions that operate reliably in production environments. Technical product management: Ability to translate ambiguous business challenges into clear product requirements, practical technical solutions, and measurable outcomes. Strong systems thinking: Ability to understand dependencies, assess downstream consequences, evaluate operational impact, and determine when a solution should be generalized across use cases. Data-driven decision-making: Experience defining measurable operational problems, establishing success metrics, analyzing data, and validating whether implemented solutions deliver the intended results. Engineering collaboration: Ability to work closely with engineering teams, understand technical trade-offs, and guide the transition from experimentation to scalable production systems. Clear communication: Strong written and verbal communication skills, including the ability to document product decisions, technical requirements, and implementation plans concisely. Adaptability and ownership: A proactive, hands-on approach to problem-solving, with the ability to work independently, navigate ambiguity, and prioritize high-impact opportunities in a small, fast-moving team. Relevant domain experience: Experience in marketing services or developing decision engines, reporting tools, attribution systems, or similar data-driven products is advantageous. Location eligibility: Must be able to work remotely from within the United States. Visa sponsorship is not available for this position. Benefits Competitive annual salary: Base salary range of $160,000–$230,000 USD, depending on qualifications, experience, and other relevant factors. Fully remote work: Flexibility to work remotely from within the United States. High-impact ownership: Opportunity to influence the direction and evolution of an AI platform with meaningful operational and commercial impact. Hands-on innovation: Work directly with AI technologies, real-world data, and engineering teams to develop and deploy practical solutions. Collaborative environment: Join a small, hands-on product team where individual contributions directly shape product outcomes. End-to-end product influence: Participate in the full product lifecycle, from identifying problems and validating prototypes to delivering scalable production systems.