Senior Product Manager, Agents

Who are Heidi?

Heidi is building an AI Care Partner that supports clinicians every step of the way, from documentation to delivery of care.

We exist to double healthcare’s capacity while keeping care deeply human. In 18 months, Heidi has returned more than 18 million hours to clinicians and supported over 73 million patient visits. Today, more than two million patient visits each week are powered by Heidi across 116 countries and over 110 languages.

Founded by clinicians, Heidi brings together clinicians, engineers, designers, scientists, creatives, and mathematicians, working with a shared purpose: to strengthen the human connection at the heart of healthcare.

Backed by nearly $100 million in total funding, Heidi is expanding across the USA, UK, Canada, and Europe, partnering with major health systems including the NHS, Beth Israel Lahey Health, Maine General, and Monash Health, among others.

We move quickly where it matters and stay grounded in what’s proven, shaping healthcare’s next era. Ready for the challenge?

We're looking for a Product Manager who has built and shipped agent‑based AI systems. Someone who understands the profound difference between single LLM calls and orchestrated multi‑turn reasoning with tool use.

You will own the evolution of our AI capabilities from reactive assistance to proactive automation, defining how intelligent systems can safely and effectively operate in clinical environments.

You will report into Product leadership and partner closely with our engineering teams to architect clinical AI used by real clinicians.

This role will be based in our San Francisco office.

We don't care about logos; the traditional insignia of competence. We'll evaluate senior well‑credentialed candidates and young, hungry hopefuls alike.

• Architect agent systems that can reason through multi‑step problems, understanding context and making intelligent decisions about tool use

• Design orchestration frameworks and tool interfaces that enable AI to interact with complex systems safely and effectively

• Obsess over clinician workflows - understand not just what they say they want, but what would actually transform their day

• Build evaluation methodologies for agent performance, measuring accuracy, reliability, and real‑world utility in production

• Establish safety and constraint systems that ensure AI agents operate within appropriate boundaries

• Create feedback loops between agent behavior and product iteration, using real‑world performance to drive improvements

• Partner with clinical teams to understand workflows and identify opportunities for intelligent automation

If we had worked together the last 6 weeks, you would have:

• Completed a listening tour across Heidi's products, understanding our current AI architecture and identifying opportunities

• Spent a full day shadowing clinicians at one of our partner health systems

• Shipped an experiment that challenged a core assumption about clinician behaviour

• Written a product brief that engineering actually got excited about (rare, but it happens)

• Jumped into customer support tickets to understand current limitations

• 4+ years PM experience with at least 2 years building AI/ML products, specifically agent systems, autonomous workflows, or multi‑turn AI applications – but we care more about what you've built than years on the clock

• Deep technical expertise in agent architectures – you've worked with frameworks like Lang Chain, implemented reasoning patterns like ReAct or Chain‑of‑Thought, and understand tool use in production

• A romantic streak about software – you believe products should be beautiful, not just functional, and that great design can transform someone's day

• Strong understanding of LLM capabilities and limitations – you know what models can and can't do, and how to architect around constraints

• Ability to design evaluation methodologies for agent systems, including performance monitoring and failure analysis

• Systems thinking – you see products as interconnected capabilities that combine and compose, not isolated features

• Technical depth to engage meaningfully with engineers

• Experience with production AI challenges like latency…