Lead ML Engineer, Personalization & Recommendation Systems

Disney Cruise Line - The Walt Disney Company; San Francisco (CA); On-site; Confidential; Full time

Benefits offered by this job

Bonus potential

Medical benefits

Long-term incentives

Job summary

Disney Entertainment and ESPN Product & Technology seeks a Lead Machine Learning Engineer to design, implement, and productionize personalization algorithms across the recommender stack for Disney+. You will work on core ranking, user understanding models, and candidate retrieval, partnering with engineers and data scientists.

You may come from an applied science or end-to-end ML engineering background, capable of driving ideas from concept to production at scale while maintaining maintainable,

Qualifications

• 7+ years of experience building and deploying ML models in production.
• Experience with recommendation systems modeling and evaluation.
• Strong understanding of AI/LLM techniques and modern ML landscape.
• Excellent communication and collaboration across teams.

Responsibilities

• Ideate, develop, iterate, and productionize personalization algorithms across the recommendation stack.
• Apply modern AI and LLM techniques to improve recommendations and evaluation.
• Collaborate with data partners and engineers to shape data, features, and objectives.
• Define the technical vision and roadmap for personalization systems.
• Design and run offline and online experiments to improve models and systems.
• Maintain production-worthy, scalable ML infrastructure and monitoring.

Skills

7+ years ML

Applied ML science

End-to-end ML engineering

Recommendation systems

AI/LLM techniques

Strong communication

Education

BS/MS in CS/Engineering

Tools

PyTorch

TensorFlow

Databricks

Spark

SQL

Job description

Disney Entertainment and ESPN Product & Technology seeks a Lead Machine Learning Engineer to design, implement, and productionize personalization algorithms across the recommender stack for Disney+. You will work on core ranking, user understanding models, and candidate retrieval, partnering with engineers and data scientists.

You may come from an applied science or end-to-end ML engineering background, capable of driving ideas from concept to production at scale while maintaining maintainable,