Remote Data Scientist II — ML Infrastructure

Pinterest; San Francisco (CA); Hybrid; USD 114,297 - 235,319; Full time

Job summary

Pinterest is seeking an experienced Data & Applied Scientist to advance ML measurement, causal inference, and productionization at scale. You will translate research workflows into robust production pipelines using Airflow, Ray, and PyTorch, building reusable patterns across teams.

You’ll partner with ML engineers and product teams to improve tooling, metrics, and model quality, leveraging rich metadata to drive data‑driven decisions and scalable ML infrastructure.

Qualifications

• 2+ years of hands-on experience as an applied scientist, ML engineer, or software engineer with ML production experience.
• Strong Python skills and familiarity with distributed compute.
• Experience with workflow orchestration tools (Airflow, Prefect, Jenkins).
• Bachelor’s/Master’s degree in Computer Science or equivalent.

Responsibilities

• Translate research-grade DS workflows into production ML pipelines using Airflow, WandB & Ray.
• Apply and productionize causal inference methods to high-stakes measurement questions.
• Partner with ML engineers and product teams to identify tooling and measurement improvements.
• Leverage metadata and engagement signals to build data-driven frameworks for feature importance and content deindexing.
• Design and build centralized ML platform tooling for end-to-end model creation and evaluation.

Skills

ML production experience

Python

Deep ML theory

Software craftsmanship

Reproducible pipelines

Education

Bachelor’s/Master’s degree in Computer Science or related field

Tools

PyTorch

Airflow

Ray

Spark

WandB

Prefect

Jenkins

Git

Job description

Pinterest is seeking an experienced Data & Applied Scientist to advance ML measurement, causal inference, and productionization at scale. You will translate research workflows into robust production pipelines using Airflow, Ray, and PyTorch, building reusable patterns across teams.

You’ll partner with ML engineers and product teams to improve tooling, metrics, and model quality, leveraging rich metadata to drive data‑driven decisions and scalable ML infrastructure.