Data Scientist II, ML Infrastructure

About This Role

Key ResponsibilitiesAI-extracted

• 1
  Translate research-grade DS workflows into production ML pipelines using Airflow, WandB & Ray while establishing reusable patterns for other teams.
• 2
  Apply and productionize causal inference methods using the production ML stack to address high-stakes measurement questions beyond experimental capabilities.
• 3
  Build self-serve tooling to empower non-experts to derive rigorous causal insights at scale.
• 4
  Partner with ML engineers and product teams to identify opportunities for improved tooling, metrics, and measurement methods.
• 5
  Leverage Pinterest's rich metadata and engagement signals to build data-driven frameworks that improve platform efficiency and speed.

RequirementsAI-extracted

• 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 with extremely strong fundamentals that can help 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.

Perks & BenefitsAI-extracted