Principal Product Manager

Health Note is building AI-powered digital assistants that act like trusted members of the care team. Instead of rigid, disconnected software modules, our assistants gain skills over time — from handling simple reschedules to managing complex intake, referrals, medication refills, and patient access workflows.

Patients experience a natural, empathetic interaction while providers gain a system that is reliable, transparent, and continually improving. Our goal is to deliver a concierge experience for every patient and a scalable path to automation for every clinic.

Health Note is hiring a Principal Product Manager to own product definition, prioritization, and experience design for our AI Agents.  This person will define what our agents should do, what “good” looks like for patients, staff, and customers, where we will and will not customize, and how customer, patient, sales, implementation, and call-review signals should translate into product decisions, agent behavior, and ongoing product improvement.

This is a senior product role for someone who can bring structure to ambiguity and create clarity across complex customer, product, and technical inputs.  Our AI Agents sit inside real healthcare workflows, which means product decisions are not just feature decisions.  They involve scheduling logic, routing rules, escalation paths, prompts, patient expectations, customer-specific constraints, operational capacity, implementation quality, and engineering tradeoffs.  The right person will be able to absorb messy input, ask the questions needed to understand the real problem, identify patterns, set clear guardrails, write crisp requirements, and help operationalize and continuously improve AI Agents in customer environments.

You will own the AI Agents product loop: customer and patient signal, agent experience, product requirements, prioritization, implementation quality, measurement, and product decision clarity.  Your job is to make sure AI Agents have a clear product owner for what we are building, why we are building it, how we know whether it is working, and what needs to improve next.

• Ask relevant questions, identify patterns across customer feedback, call reviews, conversational logs, support issues, sales input, implementation learnings, and market signals, then turn those patterns into clear product priorities, requirements, and decisions.

• Create product and implementation guardrails so Engineering and customer-facing teams can move quickly while maintaining a consistent product standard.

• Manage the AI Agent Supervisor and product coordination function, giving their work structure, standards, and visible impact.

• Define how we measure whether AI Agents are improving before and after deployment, including evaluation criteria, regression risks, production performance, and customer impact.

• Partner with Engineering to translate agent behavior into buildable requirements, with enough AI fluency to know when the answer is prompting, workflow design, data, tooling, QA, or a product constraint.

• Communicate clearly with enterprise customers about what we are building, what we are not building, what tradeoffs exist, and how we will know whether the agent is working, in a way that builds confidence without overpromising.

• AI Agents evolve as a coherent, scalable product with clear standards for customer-specific needs, implementation quality, and ongoing improvement.

• Engineering and customer-facing teams have clear guardrails for what to build, what not to build, and when to escalate.

• Customer, patient, call-review, and implementation signal consistently turns into better agent behavior and product improvements.

• Product decisions move faster, with clearer customer expectations, stronger cross-functional alignment, and better evidence behind prioritization and agent improvement.

• 6+ years product experience in a complex, workflow-heavy product environment in healthtech.

• Ability to make product decisions inside business and technical guardrails, escalating when the tradeoff requires executive input.

• Strong product taste, especially in products where the best answer is often the simplest experience that still handles real-world complexity.

• Excellent discovery instincts and pattern recognition across messy qualitative and quantitative signals, with the judgment to separate isolated customer noise from meaningful product direction.

• Experience operationalizing AI agents or similarly complex automation in customer-facing workflows, including prompts, workflow rules, testing, evaluation, regression prevention, escalation design, measurement, and ongoing improvement.

• Strong enterprise customer communication skills, including the ability to ask useful questions, set expectations, explain tradeoffs, and build confidence without overpromising.

• Hands-on experience translating discovery and strategy into product requirements, implementation guardrails, and measurable product improvements.