Principal PM, AI Enablement

Ibotta•Denver, CO

1d•Hybrid

About The Position

Ibotta is seeking a Principal PM, AI Enablement to join our team and contribute to our mission to Make Every Purchase Rewarding. In this role, you will own the AI platform infrastructure that powers Ibotta’s AI-enabled products — building the centralized context management layer, model management capabilities, and paved roads that allow product teams across the company to build intelligent, agentic experiences on a shared foundation. This is a high-impact individual contributor role that sits at the intersection of AI infrastructure, platform product management, and enterprise-scale context systems. You are building the platform that makes every AI-powered product at Ibotta faster to develop, more reliable, and grounded in the right data. You will define how Ibotta’s rich contextual data (advertiser goals, guardrails and preferences, shopper behavior, product category tendencies, publisher configurations, brand relationships, campaign history etc) is structured, governed, and made available to any AI product that needs it. In addition, you will maintain and evolve the AI productivity infrastructure that helps Ibotta’s product managers get maximum leverage from AI tooling in their daily work. This position is located in Denver, Colorado as a hybrid position requiring 3 days in office (Tuesday, Wednesday, and Thursday). Candidates must live in the United States. Not based in Denver? We will offer a relocation bonus to help make your move to the Mile High City a smooth one.

Requirements

8+ years in product management with significant experience owning AI/ML platform products, developer platforms, or infrastructure products that serve other product and engineering teams.
BA/BS in computer science, engineering, or a related technical field required.
Deep, hands-on experience with AI infrastructure — you understand context management, prompt engineering at scale, and model evaluation. You’ve shipped AI platform capabilities, not just consumed them.
Deep understanding of the AI model landscape — you know the strengths, limitations, and ideal use cases of leading foundation models and can make informed decisions about which models to deploy for different product needs, balancing capability, cost, latency, and reliability.
Experience building centralized data or context platforms that multiple product teams build on top of. You know what it takes to make a shared layer reliable, well-documented, and actually adopted.
Expert systems thinking — you see the connections between data infrastructure, context retrieval, model selection, and product quality, and you can architect an integrated platform, not just a collection of point solutions.
Technically deep: comfortable with APIs, data pipelines, model serving architectures, and working directly with ML engineers and data scientists. You don’t need to train models, but you need to make sharp infrastructure and product trade-offs alongside those who do.
Strong product judgment for platform products — you know how to balance the needs of multiple internal consumers, manage a platform roadmap, and make principled decisions about what to standardize vs. what to leave to individual teams.
Exceptional communicator who can translate complex AI infrastructure concepts for business leaders and translate business needs into platform requirements for engineering teams.
Self-directed and ownership-oriented — this role has no direct reports but operates with senior-level influence across the entire product organization and key cross-functional partners (Engineering, Architecture, Data Science, Finance, Operations).
Measured by outcomes like platform adoption across product teams, time-to-production for new AI features, context retrieval quality, model cost efficiency, and PM productivity gains.
Applicants must be currently authorized to work in the United States on a full-time basis.
For the security of our employees and the business, all employees are responsible for the secure handling of data in accordance with our security policies, identifying and reporting phishing attempts, as well as reporting security incidents to the proper channels.

Nice To Haves

Model management platforms — evaluation frameworks, A/B testing for AI, prompt versioning, cost/quality trade-off optimization.
Building or managing MCP (Model Context Protocol) integrations, AI agent frameworks, or tool-use systems for LLMs.
Performance marketing, adtech, or retail media product environments.
Prototyping with AI tools (e.g., Claude Code, Cursor, v0, Figma Make).

Responsibilities

Own the product strategy and roadmap for Ibotta’s centralized context management infrastructure — the shared layer that makes shopper, offer, publisher, brand, and campaign context available to any AI-powered product across the company.
Define and drive the model management strategy: how Ibotta selects, deploys, evaluates, and governs the AI models powering its products.
Establish standards for prompt management, model evaluation, cost optimization, and performance monitoring at scale.
Build paved roads for agentic products — standardized patterns, guardrails, and shared services (authentication, tool use, evaluation frameworks, safety controls) so that any team building an agentic experience has a production-ready foundation rather than starting from scratch.
Partner with Engineering and Architecture to design the technical infrastructure that connects Ibotta’s data assets to AI systems — ensuring the right context is retrievable, structured, and available at inference time across products.
Define the integration patterns between Ibotta’s AI platform and its core product surfaces (Campaign Manager, IPN Portal, Prospector, consumer app) so AI capabilities are delivered consistently and reliably across the portfolio.
Establish the evaluation and observability frameworks that let Ibotta measure AI product quality, detect regressions, and iterate with confidence — including ground-truth benchmarks, human-in-the-loop feedback loops, and automated quality scoring.
Maintain and expand the suite of AI-powered skills, plugins, and MCP servers used by the product team to scale product judgment and overall effectiveness.
Develop and maintain a structured “golden context library” documenting how Ibotta’s product organization operates — optimized for AI consumption and integrated into the broader context management platform.
Drive AI tool adoption across the product team through hands-on enablement, training, and documentation.
Ensure PMs have correctly configured AI environments and appropriate access to key data sources.
Embrace and uphold Ibotta’s Core Values: Integrity, Boldness, Ownership, Teamwork, Transparency, & A good idea can come from anywhere.