# Sr. Staff Machine Learning Engineer, Content Quality

**Company:** [Pinterest](https://hotfix.jobs/companies/pinterest)
**Location:** San Francisco, CA
**Role:** ML Engineering
**Salary:** $268k – $469k/yr
**Skills:** Machine Learning, Generative AI, Content Modeling, Vlms, Spark, Hadoop, Kafka, Flink, LLMs, Big Data
**Posted:** 2026-04-27

> Leads technical strategy and architecture for content quality ML signals at Pinterest, driving GenAI safety, signal development, and cross-team adoption in ranking and decision systems. Requires expertise in scalable ML, content modeling, and cross-functional leadership.

## Job Description

## What you’ll do

- Architect and develop a roadmap and processes for building and delivering signals capturing quality and trust aspects of content at Pinterest.
- Drive safety of GenAI and Conversational use cases including safety alignment and VLMs.
- 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.
- Partner closely with ML engineers to translate ideas into production-ready signals, from problem formulation and feature design to validation and deployment.

## What we’re looking for

- 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.
- Hands-on experience with big data technologies (e.g., **Hadoop**, **Spark**, **Kafka**, **Flink**) is a plus.
- Machine Learning at scale deployment experience (note this is different from having ML theoretical knowledge, which is a nice to have).
- Thought Leadership: Publication and/or conference speaking experience is a plus.

**Nice to have:**

- 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.

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