Data Scientist II, ML Infrastructure

Pinterest • Palo Alto, CA

12d • Remote

About The Position

This role focuses on advancing the science and systems behind ML measurement, feature understanding, and causal inference at scale. The work spans areas such as production feature importance platforms, observational causal estimation in Pytorch, large-scale proxy metric development, and data-driven approaches to ML infrastructure efficiency. We're looking for an enthusiastic individual contributor to perform high-impact technical work across this space. This person will drive foundational innovations, own the end-to-end design of production ML systems, establish rigorous methodological standards, and partner cross-functionally to turn successful research into durable platform capabilities that raise the ceiling for the entire ML organization.

Requirements

• 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.

Responsibilities

• 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 This Job Offers

• Job Type: Full-time
• Career Level: Mid Level