Embedded Machine Learning Engineer Jobs in Pasadena, CA

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Disney Entertainment & ESPN Technology

On any given day at Disney Entertainment & ESPN Technology, we're reimagining ways to create magical viewing experiences for the world's most beloved stories while also transforming Disney's media business for the future. Whether that's evolving our streaming and digital products in new and immersive ways, powering worldwide advertising and distribution to maximize flexibility and efficiency, or delivering Disney's unmatched entertainment and sports content, every day is a moment to make a difference to partners and to hundreds of millions of people around the world.

A few reasons why we think you'd love working for Disney Entertainment & ESPN Technology

• Building the future of Disney's media business: DE&E Technologists are designing and building the infrastructure that will power Disney's media, advertising, and distribution businesses for years to come.

• Reach & Scale: The products and platforms this group builds and operates delight millions of consumers every minute of every day - from Disney+ and Hulu, to ABC News and Entertainment, to ESPN and ESPN+, and much more.

• Innovation: We develop and execute groundbreaking products and techniques that shape industry norms and enhance how audiences experience sports, entertainment & news.

Product Engineering is a unified team responsible for the engineering of Disney Entertainment & ESPN digital and streaming products and platforms. This includes product engineering, media engineering, quality assurance, engineering behind personalization, commerce, lifecycle, and identity.

Job Summary:

ESPN is investing in largescale data infrastructure and realtime processing platforms that power nextgeneration personalization and live sports experiences. As a Machine Learning Engineer, you will focus on building and operating distributed data and ML infrastructure that supports highthroughput, lowlatency data processing and realtime ML use cases.

In this role, you will work closely with senior MLEs, data engineers, platform/SRE, and product teams to develop streaming data pipelines, feature computation systems, and MLadjacent services that operate reliably at scale. The role emphasizes handson engineering, strong fundamentals in distributed systems, and practical experience operating production data infrastructure.

Responsibilities and Duties of the Role:

1) Large-Scale Data Processing & Streaming Systems

• Build and maintain highthroughput batch and streaming data pipelines to support ML, analytics, and realtime decisioning use cases.

• Implement data ingestion, enrichment, aggregation, and transformation workflows using modern distributed data frameworks.

• Ensure pipelines meet latency, reliability, and data quality requirements for downstream ML and product teams.

2) RealTime Data & Feature Infrastructure

• Develop and operate systems that support realtime feature computation and delivery for online ML services.

• Work with feature stores and eventdriven architectures to ensure consistency between offline and online data.

• Improve data freshness, schema evolution, and backward compatibility in streaming environments.

3) ML-Adjacent infrastructure & Platform Engineering

• Build and operate MLadjacent services such as inference inputs, feature APIs, and data access layers.

• Contribute to scalable service patterns including autoscaling, rollout strategies, and resiliency mechanisms.

• Partner with platform/SRE teams to improve system availability, performance, and cost efficiency.

4) Reliability, Observability & Operations

• Instrument data and ML infrastructure with metrics, logging, and alerting to support production operations.

• Participate in oncall rotations and incident response for data and ML platforms.

• Identify and remediate data pipeline failures, performance regressions, and operational risks.

3) Collaboration & Engineering Execution

• Collaborate with applied ML and data science teams to enable production ML workflows through reliable data systems.

• Participate in design reviews, code reviews, and technical discussions.

• Follow established platform standards and contribute incremental improvements over time

Required Education, Experience/Skills/Training:

Basic Qualification:

• Experience building and operating largescale data or ML systems in production.

• Strong fundamentals in distributed systems and data processing architectures.

• Handson experience with streaming and batch data technologies (e.g., Kafka, Kinesis, Spark, Flink, or equivalent).

• Proficiency in Python and working knowledge of Java, Scala, Go, or C++.

• Experience operating systems in cloudnative environments (AWS, containers, Kubernetes, IaC tools).

• Familiarity with observability and operational best practices for production systems.

• Strong collaboration skills and ability to work effectively across engineering and data teams

Preferred qualification:

• Experience supporting realtime personalization, recommendation, or analytics systems.

• Familiarity with feature stores, eventdriven architectures, and realtime ML pipelines.

• Exposure to ML infrastructure concepts such as inference pipelines, data validation, and model lifecycle tooling.

• Experience optimizing data systems for latency, throughput, and cost efficiency.

• Understanding of experimentation platforms and data instrumentation for online systems.

Experience with:

• 5+ years of industry experience building dataintensive or MLadjacent systems in production

Required Education

• Bachelor's or Master's degree in Computer Science, Data Engineering, Machine Learning, or a related field

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