Lead ML Engineer: Personalization & Recommendations

5014 Disney Entertainment & Sports LLC; California (MO); On-site; USD 187,900 - 252,000; Full time

Job summary

Disney Entertainment and ESPN Product & Technology in San Francisco, CA seeks a Lead Machine Learning Engineer to ideate, develop, iterate on, and productionize personalization algorithms across the recommendation stack—core ranking, content understanding models, and graphs. You will prototype, validate, and maintain scalable systems with product, engineering, and data partners.

The role blends applied ML science with end-to-end ML engineering, requiring leadership in experiments, evaluation,

Qualifications

• 7+ years of experience developing machine learning models and deploying them to production systems.
• Experience in applied ML and end-to-end ML engineering, with emphasis on recommendation systems.
• Hands-on experience with AI and LLM techniques and understanding of modern AI landscape.

Responsibilities

• Ideate, develop, iterate on, and productionize personalization algorithms across the recommendation stack.
• Apply modern AI and LLM techniques to improve recommendations and evaluation.
• Contribute ideas on recommendation approaches and data/feature/goal definition.
• Drive the technical vision and innovation agenda for personalization.
• Design and run offline and online experiments to improve evaluation systems.

Skills

ML model development

End-to-end ML engineering

Recommendation systems

AI/LLM techniques

Strong communication

Collaborative work style

Education

BS in Computer Science, Engineering, or related field

MS in Computer Science, Engineering, or related field

PhD in computer science or related field

Tools

PyTorch

TensorFlow

Databricks

Spark

SQL

Job description

Disney Entertainment and ESPN Product & Technology in San Francisco, CA seeks a Lead Machine Learning Engineer to ideate, develop, iterate on, and productionize personalization algorithms across the recommendation stack—core ranking, content understanding models, and graphs. You will prototype, validate, and maintain scalable systems with product, engineering, and data partners.

The role blends applied ML science with end-to-end ML engineering, requiring leadership in experiments, evaluation,