Sr. Staff Machine Learning Engineer, Content Ecosystem

Pinterest•San Francisco, CA

5d•$227,871 - $469,147•Remote

About The Position

Pinterest works when the content ecosystem works: when people can reliably find ideas that feel inspiring, trustworthy, and actionable—and when the ecosystem continuously learns what to create, surface, and sustain next. In this Sr. Staff ML Engineer role, you’ll be the technical lead shaping how Pinterest understands and improves its content as a living marketplace: a dynamic system with feedback loops between users, creators/publishers, distribution, and long-term business outcomes. You will define a durable ML strategy that goes beyond “engagement metrics” to improve overall ecosystem health—identifying where we’re underserving content, uncovering the attributes that make content succeed, and designing optimization approaches that balance relevance, quality, diversity, integrity, and monetization. The problems are inherently multi-objective and long-horizon: the best decisions today should strengthen the ecosystem tomorrow. If you’re excited by high-leverage technical leadership, rigorous ML thinking, and marketplace-style dynamics at scale, this role offers a chance to directly shape Pinterest’s moat and the experience millions of people come to for ideas they can act on.

Requirements

Strong fundamentals in machine learning and optimization, with the ability to apply them to real-world, high-scale ecosystem problems.
Demonstrated ability to lead technical strategy, navigate ambiguity, and deliver end-to-end impact.
Deep interest in marketplace dynamics (multi-sided incentives, feedback loops, long-term health metrics), and comfort with multi-objective tradeoffs.
Experience with 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.
Degree in Computer Science, Engineering, a related field or equivalent experience.

Nice To Haves

Background in game theory, reinforcement learning, mechanism design, or causal inference applied to ecosystems/marketplaces.

Responsibilities

Set technical strategy and vision for ML systems that improve the end-to-end content ecosystem, including supply, distribution, and engagement/utility outcomes.
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.
Identify and close content gaps by building models and insights that answer: what content is missing, for whom, in which contexts, and why.
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.
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.
Design multi-objective optimization approaches that manage tradeoffs across relevance, quality, diversity, creator incentives, integrity/safety, and monetization.
Partner closely with cross-functional teams (Product, Data Science, UX Research, Content/Creator teams, Trust & Safety, Ads, Infra) to translate ambiguous ecosystem problems into clear technical roadmaps and deliver measurable impact.
Mentor and grow junior ML engineers through technical coaching, design reviews, career development support, and creating a culture of strong engineering and scientific rigor.
Raise the quality bar for ML engineering by establishing best practices for data quality, model governance, reliability, privacy-aware design, and operational excellence.
Communicate clearly and influence broadly by producing crisp technical proposals, aligning stakeholders on tradeoffs, and driving decisions across org boundaries.
Explore and apply advanced methods where beneficial—e.g., game-theoretic approaches, reinforcement learning, mechanism design, or bandit-style optimization—to improve marketplace dynamics and long-term ecosystem outcomes.