Principal Product Manager, Machine Learning Platform

Principal Product Manager, Machine Learning Platform

Join to apply for the Principal Product Manager, Machine Learning Platform role at People Inc.

We’re seeking a Principal Platform Product Manager to drive the evolution of our internal platforms that power content discovery and publishing experiences across People Inc.'s 30+ brands, reaching over 100M users monthly. This role sits at the intersection of engineering excellence and product strategy—focused on building scalable, reusable ML‑enabling services that accelerate development velocity, reduce duplication, and improve consistency across teams.

This role reports to the VP of Product, Core Platform, and collaborates closely with Data Services and Platform Engineering leadership to ensure we’re solving the right problems with the right strategic focus.

Hybrid 3x a week – (New York, NY). In‑office expectations: this position is hybrid in‑office, with the ability to work remotely for up to 2 days per week.

• Strategic Road mapping
  • Define and maintain a platform roadmap aligned with company‑wide goals and engineering priorities.
  • Prioritize investments that reduce redundant work, improve developer satisfaction, and unlock new end‑user capabilities.
  • Make deliberate tradeoffs—focusing as much on what not to build as what to include to preserve platform coherence.
  • Act now not later, move with urgency while staying aligned to the long‑term vision.
• Platform‑as‑a‑Product Thinking
  • Champion the platform as a product, not just a set of tools—requiring careful, opinionated curation.
  • Avoid the “feature shop trap” by recognizing patterns in requests and designing extensible, self‑service capabilities.
  • Promote modularity, standardization, and reusability across services to reduce fragmentation and technical debt.
  • Deliver with intention, by thoughtfully balancing user problems with platform coherence.
• Cross‑Org Orchestration
  • Lead multi‑team initiatives for search, recommendations, content enrichment, and LLM infrastructure.
  • Serve as the connective tissue between platform engineering, infrastructure, data science, and consuming teams.
  • Collaborate respectfully to align stakeholders around shared technical outcomes.
• Internal Developer Experience
  • Treat internal developers as primary customers—improving API usability, documentation, and onboarding.
  • Build trusted, self‑serve services with intuitive interfaces and strong feedback loops.
  • Make it matter by solving real developer pain points with empathy and insight.
• Migration Strategy & Adoption
  • Experience designing compelling migration paths and driving adoption of new platform capabilities.
  • Ability to incentivize teams to adopt platform services through demonstrable value through efficiency, reliability, or new functionality.
  • Support teams throughout the transition process to reduce total cost of ownership.
  • Champion accountability in getting teams successfully onto future‑proof platforms.
• Operational Excellence
  • Partner with engineering to ensure platform reliability, stability, and performance.
  • Champion SLOs, observability, and self‑healing capabilities as foundational product attributes.
  • Avoid operational burden falling to users by proactively addressing edge cases and failure modes.
  • Expect integrity in how we build, test, and scale platform services.
• Measurement & Outcomes
  • Define and track platform leverage metrics—e.g., engineering hours saved, duplicated work eliminated, system uptime.
  • Balance platform health metrics with adoption, developer satisfaction, and impact to end‑user outcomes.
  • Build feedback loops to continuously iterate on platform capabilities.
• Innovation Enablement
  • Identify and prioritize future‑facing opportunities like vector search and LLM‑ready APIs.
  • Serve as a force multiplier by enabling faster, more innovative delivery across the org.
  • Embrace change to grow–pioneer forward‑thinking technical capabilities that empower teams.

• Education: Bachelor’s degree required in Computer Science, Engineering, Business, or related field and/or equivalent experience.
• Experience: 7+ years in product management including experience delivering ML‑powered platforms.
• Specific Knowledge, Skills,…