Ai Product Manager Jobs

About The Position

Requirements

• Bachelors Degree in Engineering, Computer Science, or a related technical field
• 6+ years of product management experience, with 3+ years owning data, analytics, or platform products
• Demonstrated pattern recognition across products: you have taken data capabilities built for one customer or team and turned them into shared products others adopted
• Deep technical fluency: you can go toe-to-toe with engineers and data engineers on pipelines, medallion architectures, semantic layers, and APIs, and you can read a schema and write SQL
• Experience defining and governing metrics: KPI catalogs, semantic layers, or analytics products at scale
• Experience with AI/ML evaluation or model quality: eval frameworks, offline/online testing, or monitoring of models in production
• Experience with enterprise data integration; EHR data (Epic Clarity/Caboodle, HL7, FHIR) is a strong plus
• Experience shipping products in regulated environments (healthcare, finance, or similar)
• Excellent communication skills: you can explain data architecture tradeoffs to executives and product requirements to engineers, and you can influence teams you do not manage
• Comfort with ambiguity and ability to make decisions with incomplete information
• Able to work onsite in Palo Alto 3 days/week

Nice To Haves

• Understanding of healthcare operations, health system IT environments, clinical data models, or AI governance frameworks (NIST AI RMF, ISO 42001).

Responsibilities

• Own the roadmap for data and analytics products, balancing customer commitments, internal builder needs, and platform reuse
• Map every analytics question to its source system (product databases, event analytics, observability tooling, the lakehouse) and own the architecture decisions for how data lands in the serving layer: tables, granularity, fields, cadence
• Turn per-customer data builds into reusable data products with clear interfaces, so the second customer costs a fraction of the first
• Define and enforce generalization requirements for scored and modeled outputs: what was validated on hundreds of patients must hold at hundreds of thousands
• Write complete acceptance criteria before work commits to a sprint; no ambiguity reaches engineering or data
• Run the vetting process for customer-facing KPIs and own the metrics catalog end to end
• Own cost observability as a product: every model call attributed, every workflow priced, actuals replacing estimates
• Define the evaluation gates for AI-powered products (validation, generalization at scale, post-go-live monitoring) and own them as reusable platform capability rather than per-product improvisation
• Own the governance product surface: audit trails, monitoring, and the evidence customers and their compliance teams need to trust AI in production
• Partner with the data platform and analytics leads to establish and maintain ownership contracts for ETL, business logic, and integration surfaces
• Define success metrics for reuse and data product health (component adoption, time-to-second-deployment, ingestion completeness, metric consistency) and use them to drive prioritization
• Navigate healthcare regulatory requirements (HIPAA, data privacy, clinical safety) as first-order product constraints

Benefits

• competitive salaries with equity packages
• robust medical/dental/vision insurance
• flexible working hours
• hybrid work options