Principal ML Architect - Personalization & Platform

The Walt Disney Company; Glendale (CA); On-site; USD 207,000 - 278,000; Full time

Job summary

The Walt Disney Company in Glendale, California seeks a Principal Machine Learning Engineer to define and own the architecture of the N&E ML Platform across Disney brands. You will drive personalization, recommendations, and ML infrastructure, partnering with senior leadership and cross‑org engineering.

You will mentor engineers, spearhead reliability and observability, and push for scalable, low‑latency ML solutions that connect ML investments to guest experiences and business outcomes.

Qualifications

• Bachelor's degree or equivalent in a related field.
• 10+ years building and operating ML systems in production.
• Deep expertise in data science, deep learning, and statistics.
• Experience owning architecture across a large platform or domain.
• Proven ability to drive measurable ML improvements.
• Experience with REST-based backend microservices and cloud infra.

Responsibilities

• Define end-to-end architecture for the N&E ML platform across Disney brands.
• Prioritize ML workstreams and surface risks to leadership.
• Design ML lifecycle infra: data pipelines, orchestration, feature stores, online serving.
• Stay current with ML trends and champion new tech adoption.
• Own incident response and drive reliability improvements.
• Mentor senior engineers and influence cross-org standards.

Skills

Architecture ownership

ML platform design

Cloud infra AWS

Big data tech

Leadership & mentorship

Agile/Scrum

Education

Bachelor’s degree in CS/IS/Stats/Math

Tools

Databricks

Spark

Kinesis

Kafka

REST microservices

Job description

The Walt Disney Company in Glendale, California seeks a Principal Machine Learning Engineer to define and own the architecture of the N&E ML Platform across Disney brands. You will drive personalization, recommendations, and ML infrastructure, partnering with senior leadership and cross‑org engineering.

You will mentor engineers, spearhead reliability and observability, and push for scalable, low‑latency ML solutions that connect ML investments to guest experiences and business outcomes.