Senior ML Engineer, Personalization & Recommendations

Disney Entertainment and ESPN Product & Technology; New York (NY); On-site; USD 150,000 - 190,000; Full time

Benefits offered by this job

Medical benefits

Financial benefits

Bonus and or long-term incentive units

Job summary

Disney Entertainment and ESPN Product & Technology in New York, NY is seeking a senior individual contributor to develop, productionize, and refine personalization and recommendation algorithms for Disney+ and Hulu. The role collaborates with Engineering, Product, and Data teams to deliver scalable ML solutions.

You will own end-to-end ML pipelines from feature engineering to deployment, build data infrastructures with Airflow, Databricks, and Spark, and uphold production-quality standards.

Qualifications

• 5+ years of experience developing ML models or data engineering.
• 5+ years writing production-grade Python and SQL.
• 3+ years deploying algorithms to production systems.
• Experience with large-scale data, cloud platforms, and data pipelines.

Responsibilities

• Algorithm development and maintenance for personalization and recommendations.
• Build and maintain ETL pipelines with Airflow and Jenkins; deploy data pipelines at scale.
• Establish and uphold standards for development, testing, and deployment.
• Collaborate with product, data teams to improve data collection and experimentation.

Skills

Strong communication skills

Production ML experience

End-to-end ML pipeline experience

Education

MS or PhD in statistics, mathematics, computer science, or related quantitative field

Tools

Python

SQL

Airflow

Jenkins

Databricks

S3

Spark

Job description

Disney Entertainment and ESPN Product & Technology in New York, NY is seeking a senior individual contributor to develop, productionize, and refine personalization and recommendation algorithms for Disney+ and Hulu. The role collaborates with Engineering, Product, and Data teams to deliver scalable ML solutions.

You will own end-to-end ML pipelines from feature engineering to deployment, build data infrastructures with Airflow, Databricks, and Spark, and uphold production-quality standards.