Product Manager, AI Devices

Imagen Technologies

11d•$125,000 - $175,000•Remote

About The Position

As the Product Manager for AI Devices, you will own the evolution of our AI-enabled diagnostic workflows. Your mission is to bridge the gap between advanced AI capabilities and seamless physician experiences, ensuring use of our AI technology becomes the standard of care within our practice. You will be the primary driver of efficiency, using data to drive down unit costs while maintaining exceptional quality of care and physician satisfaction.

Requirements

3-6 years of experience in product management, preferably with a focus on healthcare software/platform products.
Problem-Solving Mindset: A proven track record of breaking down complex problems and driving to elegant solutions.
Technical Product Execution: Demonstrated experience in an agile/scrum development environment, with a strong ability to write clear requirements and manage a backlog.
Technical AI Fluency: Sufficiently technical to understand system architectures and basic AI/Machine Learning concepts.
Data & Analytics Mastery: Demonstrated ability to analyze complex datasets to derive actionable insights regarding user behavior and operational efficiency.
Stakeholder Management: Exceptional ability to manage cross-functional processes and communicate effectively with both high-level stakeholders and engineering teams.
Platform Leadership: Experience driving a platform backlog and setting quarterly roadmaps that align with broad company objectives.

Nice To Haves

Healthcare Domain Knowledge: Familiarity with healthcare-specific standards (DICOM, HL7) and imaging systems (PACS, VNA) is highly preferred.

Responsibilities

Own the AI-Driven Roadmap: Define and manage the detailed product roadmaps for new AI products across X-ray, CT, MRI, and Ultrasound—from initial concept through launch sequencing and ongoing iteration.
Drive Modality Deployments: Lead end-to-end deployment of new AI products for each modality, coordinating cross-functional readiness across AI, clinical quality, clinical operations, data, and engineering.
Own Data Product Requirements: Partner closely with Data Engineering to define requirements for data quality, preprocessing pipelines, storage, and architecture that enable reliable model training and production performance.
Facilitate Cross-Functional Execution: Serve as the day-to-day connector between AI, clinical quality, clinical operations, and data teams—helping lead recurring forums to align on decisions, unblock issues, and maintain momentum.
Manage Timelines and Resourcing: Build and maintain integrated timelines and resource plans, including headcount needs and key OPEX inputs (compute, storage, and data transfer), surfacing tradeoffs early.
Build the User Feedback Loop: Establish a repeatable process to gather user feedback post-deployment and translate it into a prioritized backlog of product improvements and experiments.
Define and Analyze Performance Metrics: Establish KPIs and dashboards to evaluate product performance, quantify operational impact, and package results for leadership, board, investors, and marketing narratives.