Director of Product Management - US Remote

PerfectServe is a leading provider of clinical communication and physician scheduling solutions in the health IT space. Founded in 1997, the company has grown significantly, particularly after several major acquisitions in 2019. With 400+ employees, 30,000+ customers, and $100 million+ in annual revenue, PerfectServe aims to accelerate speed to care by optimizing provider schedules and routing communications efficiently.

About the role

The Director of Product Management – AI at PerfectServe will own the product vision, strategy, and lifecycle of the company's AI-native answering service platform. This role involves defining the product strategy, translating business objectives and clinical workflows into a prioritized roadmap, and ensuring alignment with technical capacity and infrastructure investments.

Responsibilities

• Define and own the product strategy for PerfectServe's AI answering service, including near-term delivery commitments and a longer-horizon vision for AI evolution across the portfolio.
• Translate business objectives and clinical workflows into a prioritized, clearly reasoned product roadmap and maintain it as a living document reflecting new information.
• Partner with the CTO and Director of Software Engineering to align roadmap sequencing with technical capacity, architecture constraints, and infrastructure investments.
• Make and communicate clear product tradeoff decisions regarding what is in scope, what is deferred, and why.
• Lead customer discovery efforts through interviews, site visits, and workflow observation sessions with clinical and administrative end users.
• Develop and maintain a deep understanding of healthcare organizations' use of answering services, operator consoles, and AI-augmented communication, including pain points, workarounds, and compliance constraints.
• Synthesize input from cross-functional teams and stakeholders into product insights, distinguishing signal from noise when stakeholder demands conflict.
• Define user personas and jobs-to-be-done for the AI platform's primary audiences: clinical staff, contact center agents, patients, and healthcare administrators.
• Use modern AI tools to accelerate product work, generate prototypes and wireframes, run rapid concept tests, and explore technical feasibility before committing engineering resources.
• Design and run structured product experiments, including A/B tests, pilot programs, and shadow deployments, to validate hypotheses before broad rollout.
• Define clear acceptance criteria and success metrics for every feature, including AI-specific measures such as containment rate, deflection accuracy, latency, and user-perceived quality.
• Evaluate emerging AI capabilities and recommend adoption decisions based on product value and technical fit, not novelty.
• Apply working knowledge of healthcare interoperability standards (HL7, FHIR, and API integration patterns) to shape AI feature design and integration requirements.
• Ensure AI features are designed with clinical workflow reality in mind, considering how nurses, physicians, schedulers, and operators interact with systems under time pressure.
• Identify and manage product-level compliance and safety considerations, including HIPAA, minimum necessary data principles, and the appropriate use of AI in clinical decision-adjacent contexts.
• Collaborate with Clinical and Operations teams to ensure AI behavior aligns with care protocols, escalation paths, and patient safety standards.
• Own the relationship between product requirements and engineering delivery, writing precise, well-scoped specifications that reduce ambiguity without over-constraining implementation.
• Collaborate with Marketing and Customer Success on go-to-market sequencing for new AI features, including packaging, positioning, and customer communication.
• Represent the product perspective in executive conversations, communicating roadmap priorities, tradeoffs, and AI adoption risks clearly to non-technical stakeholders.
• Define the measurement framework for AI product performance, establishing baselines, setting targets, and driving quarterly reviews of key metrics with engineering and executive leadership.
• Build a culture of learning from production data, using real-world call outcomes, error logs, and user feedback to continuously improve AI model performance and product design.
• Identify and act on leading indicators of customer churn or dissatisfaction related to AI feature quality.
• Initially operate as an individual contributor, building out a product and design team as the platform and organization scale.
• Establish PM practices and norms that support AI-native development, including lightweight documentation, fast feedback loops, and outcome-oriented delivery measurement.
• Mentor and develop junior product team members as the function grows.

Qualifications

• 8+ years of product management experience, with at least 3 years owning AI, NLP, or conversational AI products in production.
• Demonstrated ability to lead customer discovery in complex B2B environments; experience interviewing clinical or operational end users is a strong differentiator.
• Working knowledge of healthcare interoperability standards: HL7 v2, FHIR, and API integration patterns. Familiarity with HIPAA and the product design considerations it requires: data minimization, audit trails, access controls, and PHI handling.
• Technical fluency sufficient to evaluate AI capabilities critically, write clear functional specifications, and prototype concepts using no-code or AI-assisted tools.
• Track record of shipping AI features at scale and iterating based on production data and user feedback.
• Strong written communication; able to produce clear PRDs, executive summaries, and customer-facing feature descriptions.
• Experience working with outsourced development partners, including delivery accountability and quality review.

Preferred Experience

• Hands-on familiarity with conversational AI platforms, voice agent frameworks, or telephony product design.
• Exposure to LLM evaluation practices, prompt engineering workflows, or AI product safety frameworks.
• Experience in a growth-stage company where product practice is still being built, not just inherited.
• Ability to prototype using tools such as Figma, Claude, Replit, or equivalent AI-assisted development environments.