Sr. Staff Machine Learning Engineer, Content Ecosystem

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

Sr. Staff ML Engineer at Pinterest focused on improving the content ecosystem through ML strategy, measurement frameworks, and optimization approaches. The role involves building models to identify content gaps, understanding content success factors, and optimizing marketplace mechanisms for long-term ecosystem value. It requires technical leadership, rigorous ML thinking, and experience with multi-objective optimization and marketplace dynamics.

What you'd actually do

  1. Set technical strategy and vision for ML systems that improve the end-to-end content ecosystem, including supply, distribution, and engagement/utility outcomes.
  2. Partner with DS teams to develop a content ecosystem measurement framework to quantify content health and performance (e.g., content quality, freshness, diversity, coverage, creator/content sustainability, and user value), and align it with company/business goals.
  3. Identify and close content gaps by building models and insights that answer: what content is missing, for whom, in which contexts, and why.
  4. Deeply understand what content works and why by combining causal thinking, experimentation, and model interpretability to connect content attributes and distribution mechanisms to downstream user and business outcomes.
  5. Build and optimize content marketplace mechanisms that balance multi-sided incentives and constraints (e.g., users, creators/publishers, advertisers, internal policy/safety), while maximizing long-term ecosystem value.

Skills

Required

  • Machine learning fundamentals
  • Optimization fundamentals
  • Technical strategy leadership
  • Ambiguity navigation
  • End-to-end impact delivery
  • Marketplace dynamics understanding
  • Multi-objective tradeoff management
  • Causal thinking
  • Experimentation
  • Model interpretability
  • MLOps best practices
  • Data quality
  • Model governance
  • Reliability
  • Privacy-aware design
  • Operational excellence
  • Communication
  • Stakeholder alignment

Nice to have

  • Game theory
  • Reinforcement learning
  • Mechanism design
  • Causal inference applied to ecosystems/marketplaces
  • LLM-powered productivity tools

What the JD emphasized

  • technical strategy
  • ML systems
  • content ecosystem
  • measurement framework
  • content health
  • content gaps
  • model interpretability
  • marketplace mechanisms
  • multi-objective optimization
  • ecosystem value
  • long-term ecosystem outcomes
  • high-scale ecosystem problems
  • technical leadership
  • rigorous ML thinking
  • marketplace dynamics
  • multi-objective tradeoffs

Other signals

  • ML strategy for content ecosystem
  • multi-objective optimization
  • marketplace dynamics
  • long-term ecosystem health