Data Scientist II, ML Infrastructure

📋 Description

• Translate research DS workflows into production ML pipelines with Airflow, WandB, Ray.
• Productionize causal inference methods (propensity scoring, IPW, TMLE) at scale.
• Partner with ML engineers and product teams to improve tooling, metrics, and methods.
• Leverage signals to build data-driven frameworks for feature importance and deindexing.
• Design centralized ML platform tooling for scalable feature and model creation.

🎯 Requirements

• 2+ years in applied science or ML engineering with production ML experience.
• Strong Python skills; PyTorch experience; knowledge of distributed compute (Spark, Ray).
• Deep ML theory knowledge and first-principles reasoning.
• Software dev best practices including version control, code reviews, and reproducible pipelines.
• Experience with workflow tools (Airflow, Prefect, Jenkins) for ML pipelines.
• Bachelor’s or Master’s in CS or related field.

🎁 Benefits

• Remote-friendly role with 3-5 in-person days per quarter.
• Equity opportunity.
• PinFlex program for flexible work.
• Inclusive, equal-opportunity employer.
• Work on ML infrastructure at scale.