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# Data Scientist II, ML Infrastructure

- **Company:** Pinterest
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $114,297.0 - $235,319.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Code Review, Python (Programming Language), Machine Learning, Azure Machine Learning, Workflow Management Systems, Pytorch, Apache Spark, Deep Learning, Build Management, Information Technology, Machine Learning Operations, Software Version Control, Data Pipelines, Jenkins
- **Published:** July 26, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=b93810d0bc9fc1f6

## About the Role

* 2+ years of hands-on experience as an applied scientist, ML engineer, research scientist or software engineer, with significant ML production experience.
 * Strong Python skills; experience with PyTorch or equivalent deep learning frameworks; familiarity with distributed compute (Spark, Ray). Ray specifically is a strong plus.
 * Enthusiasm for building tools and platforms that multiply the impact of an entire ML organization; not just solving one-off problems.
 * Deep ML theory knowledge with extremely strong fundamentals that can help us reason about ML models from first principles.
 * Proficiency in software development best practices including version control, code review, and reproducible ML pipelines.
 * Experience with workflow management tools (Airflow, Prefect, Jenkins, or similar) for reliable ML pipeline orchestration.
 * Bachelor's/Master's degree in a relevant field such as Computer Science, or equivalent experience.

## Description

We are looking for an experienced and highly capable Data & Applied Scientist to help us drive step function improvements in our ML capabilities at Pinterest.

In this role, you will:

 * Translate research-grade DS workflows (e.g., proxy metrics, staleness models) into production ML pipelines using Airflow, WandB & Ray while establishing reusable patterns for other teams.
 * Apply and productionize causal inference methods using the production ML stack (propensity scoring, IPW, TMLE) to address high-stakes measurement questions beyond experimental capabilities. Build self-serve tooling to empower non-experts to derive rigorous causal insights at scale.
 * Partner with ML engineers and product teams to identify opportunities for improved tooling, metrics, and measurement methods, unlocking step-change improvements in model quality and business outcomes.
 * Leverage Pinterest's rich metadata and engagement signals to build data-driven frameworks, from feature importance to content deindexing, that improve platform efficiency and speed.
 * Design and build centralized ML platform tooling to improve feature and model creation, evaluation, and trust, including production systems that operate daily at scale across all models., * We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.
 * This role will need to be in the office for in-person collaboration 3-5 times/quarter and therefore can be situated anywhere in the country.


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