Principal ML Engineer — Architect of Scalable Personalization

5014 Disney Entertainment & Sports LLC

Glendale (CA)

On-site

USD 207,000 - 278,000

Full time

8 days ago

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Job summary

Disney Entertainment and ESPN Product & Technology is seeking a Principal Machine Learning Engineer to own the architecture and strategic direction of the N&E ML Platform across a large, complex problem space. You will drive step-function improvements in personalization, recommendations, and ML infrastructure, partnering with senior leadership and cross-org teams to set the standard for ML excellence.

You will mentor engineers, drive reliability, and align ML initiatives with business outcomes

Qualifications

Bachelor’s degree in computer science, information systems, statistics, math, or related field, or equivalent experience.
10+ years building and operating ML engineering systems in production environments.
Deep expertise in data science, deep learning, and statistical methods for large-scale problems.
Experience owning architecture across a major platform or product domain, including architecture documents and reviews.
Proven ability to drive quantifiable ML platform improvements and personalized experiences.
Experience designing and evolving backend microservices using REST.
Strong cloud infrastructure expertise, especially AWS.
Hands-on experience with Databricks, Spark, Kinesis, and Kafka.
Proven incident response leadership and reliability programs.
Active participation in cross-organizational engineering standards and governance.
Metrics-driven leadership linking engineering decisions to business outcomes.
Excellent communication and collaboration with senior leadership and stakeholders.
Experience in Agile/Scrum with strong prioritization and stakeholder management.

Responsibilities

Define and own end-to-end architecture of the N&E ML platform across a large problem space.
Identify, scope, and prioritize impactful ML workstreams across the portfolio.
Drive the design and evolution of ML infrastructure for data pipelines, orchestration, feature stores, and online serving.
Stay at the forefront of ML trends and advocate adoption of new technologies.
Own production incident response and drive reliability improvements.
Engage with cross-organizational communities to set standards and governance.
Lead metrics-driven programs connecting ML investments to business outcomes.
Mentor senior engineers and foster ownership and rigor.