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$160,000 – $230,000/yr

Principal Product Manager, AI Platform

United StatesRemoteFull-timelead

Product ManagementPrincipal Product Manager

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Quick Summary

Requirements Summary

Develop frameworks to measure and address challenges such as content decay, using data and actionable metrics to identify opportunities for continuous improvement.

Technical Tools

Product ManagementPrincipal Product Manager

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

~2 min read

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.

What We Offer

~2 min read

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.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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✓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.