Jobs · Marketing · New York

Senior Product Manager (Artificial Intelligence)

Fabric · New York, NY · 3 wk ago
HybridMarketing$140k–$165k/yrFull-time

About the role

We are looking for a Senior Product Manager, AI to own the AI-powered experiences that sit at the heart of how clinicians deliver care and how members navigate it. You will lead product strategy across both surfaces, the tools clinicians use every day and the experiences patients rely on, and translate the capability of modern AI into outcomes that actually matter in healthcare.

Responsibilities

  • Own the product strategy and roadmap for AI experiences across clinician-facing and member-facing surfaces, with clear bets, sequencing, and success criteria.
  • Partner with engineering, applied ML, design, and clinical leadership to ship AI products that move the metrics that matter: clinician time recovered, member activation, care outcomes, cost to serve.
  • Scale the AI features already in production. Measure impact rigorously, iterate on both the model and the user experience, and expand to new use cases as evidence comes in.
  • Define and operationalize the quality bar for AI in a clinical context. This includes evals, safety thresholds, hallucination guardrails, and the operational playbook for shipping responsibly.
  • Make build-versus-buy decisions on models, vendors, and infrastructure. Own the tradeoffs between latency, cost, accuracy, and risk, and make them transparently.
  • Set the operating cadence for AI product work: how experiments run, how we measure, how we ship safely, and how we communicate AI risk and reward to clinical and executive stakeholders.
  • Translate between technical ML capability and clinical workflow reality. Neither side wants to learn the other side's language, and your job is to bridge it.

Qualifications

  • 6+ years of product management experience, with at least 2 years owning AI or ML-powered products in production.
  • A track record of shipping LLM, ML, or AI-powered features to real users at meaningful scale.
  • Strong working knowledge of LLM fundamentals: evals, prompting, RAG, fine-tuning tradeoffs, and the latency-cost-accuracy curves that shape every real-world deployment.
  • Proven partnership with applied ML, AI infrastructure, or engineering teams. You speak the language and the engineers know it.
  • Comfort operating in regulated environments where safety, accuracy, and audit-readiness are not optional.
  • Excellent product judgment and the ability to make calls in ambiguity without waiting for permission.

Bonus points

  • Healthcare or clinical product experience, particularly on surfaces used by licensed providers.
  • Experience shipping consumer or patient-facing AI in regulated contexts.
  • Experience leading AI product work across both B2B (clinician) and B2C (member) surfaces.

What You'll Do

As the Senior Product Manager, AI, you will own the AI product strategy and execution for Fabric across both clinician-facing and member-facing surfaces. Your primary responsibilities will include:

  • Own the product strategy and roadmap for AI experiences across clinician-facing and member-facing surfaces, with clear bets, sequencing, and success criteria.
  • Partner with engineering, applied ML, design, and clinical leadership to ship AI products that move the metrics that matter: clinician time recovered, member activation, care outcomes, cost to serve.
  • Scale the AI features already in production. Measure impact rigorously, iterate on both the model and the user experience, and expand to new use cases as evidence comes in.
  • Define and operationalize the quality bar for AI in a clinical context. This includes evals, safety thresholds, hallucination guardrails, and the operational playbook for shipping responsibly.
  • Make build-versus-buy decisions on models, vendors, and infrastructure. Own the tradeoffs between latency, cost, accuracy, and risk, and make them transparently.
  • Set the operating cadence for AI product work: how experiments run, how we measure, how we ship safely, and how we communicate AI risk and reward to clinical and executive stakeholders.
  • Translate between technical ML capability and clinical workflow reality. Neither side wants to learn the other side's language, and your job is to bridge it.

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