# Data Scientist II, ML Infrastructure

**Company:** [Pinterest](https://hotfix.jobs/companies/pinterest)
**Location:** Palo Alto, CA
**Role:** ML Engineering
**Salary:** $114k – $235k/yr
**Experience:** 2+ years
**Skills:** Python, PyTorch, Airflow, Ray, Spark, Causal Inference, ml pipelines, wandb
**Posted:** 2026-07-23

> Build and productionize ML measurement, causal inference, and platform tooling at Pinterest. Translate research into scalable pipelines, develop self-serve causal tools, and create centralized systems for feature importance, model evaluation, and infrastructure efficiency.

## Job Description

## What you’ll do

- 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.

## What we’re looking for

- 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.

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