# Sr. Staff Machine Learning Engineer, Content Ecosystem

**Company:** [Pinterest](https://hotfix.jobs/companies/pinterest)
**Location:** Remote
**Role:** ML Engineering
**Salary:** $228k – $469k/yr
**Experience:** 8+ years
**Skills:** Machine Learning, Optimization, Causal Inference, Reinforcement Learning, Game Theory, Mechanism Design, Multi-Objective Optimization, Experimentation, Model Interpretability, Python
**Posted:** 2026-05-22

> Technical lead defining ML strategy and systems to improve Pinterest's content ecosystem health, marketplace dynamics, and long-term outcomes. Requires strong ML fundamentals, marketplace thinking, and experience leading high-scale, multi-objective optimization projects.

## Job Description

## What you’ll do
- 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.

## What we’re looking for
- 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.
- Not required but certainly a plus: background in game theory, reinforcement learning, mechanism design, or causal inference applied to ecosystems/marketplaces.
- Degree in Computer Science, Engineering, a related field or equivalent experience.

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