Data Scientist II, ML Infrastructure

Pinterest is seeking a Data Scientist II to join its ML Infrastructure team, focusing on advancing the science and systems behind ML measurement, feature understanding, and causal inference at scale.

• Translate research-grade DS workflows (e.g., proxy metrics, staleness models) into production ML pipelines using Airflow, WandB & Ray.
• Apply and productionize causal inference methods (propensity scoring, IPW, TMLE) using the production ML stack.
• Build self-serve tooling for rigorous causal insights at scale.
• Partner with ML engineers and product teams to improve tooling, metrics, and measurement methods.
• Leverage Pinterest's metadata and engagement signals to build data-driven frameworks.
• Design and build centralized ML platform tooling for feature and model creation, evaluation, and trust.

• 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).
• Deep ML theory knowledge and strong fundamentals.
• 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).
• Bachelor's/Master's degree in a relevant field such as Computer Science, or equivalent experience.

Base salary range: $114,297—$235,319 USD. The position is also eligible for equity.

This role requires in-office collaboration 3-5 times/quarter and can be situated anywhere in the US.

Apply via the Pinterest careers page.