Sr. Staff Machine Learning Engineer, Content Quality

Pinterest Pinterest · Consumer · Palo Alto, CA · Core Engineering

Sr. Staff Machine Learning Engineer, Content Quality at Pinterest. This role involves architecting and developing signals for content quality and trust, driving safety for GenAI and conversational use cases, and partnering with ML engineers to deploy production-ready signals. The role also requires aligning with downstream teams on use cases and signal adoption, and working cross-functionally to set and execute long-term strategy. Experience with big data technologies and ML at scale deployment is a plus.

What you'd actually do

  1. Architect and develop a roadmap and processes for building and delivering signals capturing quality and trust aspects of content at Pinterest.
  2. Drive safety of GenAI and Conversational use cases including safety alignment and VLMs.
  3. Work with downstream teams to align on use cases, evaluate signal impact, and drive adoption of signals in models, ranking systems, and decision-making workflows.
  4. Partner closely with ML engineers to translate ideas into production-ready signals, from problem formulation and feature design to validation and deployment.

Skills

Required

  • Experience driving technical strategy at an organizational level.
  • Expertise in content modeling at consumer internet scale.
  • Using GenAI for scaling ML development.
  • Strong ability to work cross-functionally and with partner engineering teams.
  • Experience working with multiple stakeholders.
  • Strong measurement and scalability experience.
  • Strong ML knowledge and expertise.
  • Machine Learning at scale deployment experience

Nice to have

  • Hands-on experience with big data technologies (e.g., Hadoop / Spark / Kafka / Flink) is a plus.
  • Thought Leadership: Publication and/or conference speaking experience is a plus.
  • Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring.
  • Familiarity with LLM-powered productivity tools for documentation search, experiment analysis, SQL/data exploration, and engineering workflow acceleration.

What the JD emphasized

  • Machine Learning at scale deployment experience

Other signals

  • driving technical strategy for content quality
  • architecting and developing signals for quality and trust
  • driving safety of GenAI and Conversational use cases
  • partnering with ML engineers to translate ideas into production-ready signals
  • experience with big data technologies