Senior Machine Learning Engineer

ESPN is expanding large-scale data infrastructure and real-time processing platforms to support next-generation personalization and live sports experiences. As a Senior Machine Learning Engineer at Disney Entertainment & Sports LLC, you will build and operate distributed data and ML infrastructure designed for high-throughput, low-latency processing and production ML use cases.

What you’ll do

• Build and maintain high-throughput batch and streaming data pipelines for ML, analytics, and real-time decisioning.
• Implement ingestion, enrichment, aggregation, and transformation workflows using modern distributed data frameworks.
• Deliver pipelines that meet latency, reliability, and data quality expectations for downstream ML and product teams.
• Develop and operate systems for real-time feature computation and feature delivery to online ML services.
• Use feature stores and event-driven architectures to keep offline and online data consistent.
• Improve data freshness, schema evolution, and backward compatibility in streaming environments.
• Build and operate ML-adjacent services such as inference inputs, feature APIs, and data access layers.
• Support scalable service patterns including autoscaling, rollout strategies, and resiliency mechanisms.
• Partner with platform/SRE teams to improve availability, performance, and cost efficiency.
• Instrument production systems with metrics, logging, and alerting to enable reliable operations.
• Participate in on-call rotations and incident response for data and ML platforms.
• Investigate and remediate pipeline failures, performance regressions, and operational risks.
• Collaborate with applied ML and data science teams to enable production ML workflows through dependable data systems.
• Contribute to design reviews, code reviews, and technical discussions while following platform standards.

What you bring

• 5+ years of industry experience building data-intensive or ML-adjacent systems in production.
• Proven experience building and operating large-scale data or ML systems in production.
• Strong fundamentals in distributed systems and data processing architectures.
• Hands-on experience with streaming and batch technologies such as Kafka, Kinesis, Spark, Flink, or equivalents.
• Proficiency in Python and working knowledge of Java, Scala, Go, or C++.
• Experience operating in cloud-native environments using AWS, containers, Kubernetes, and IaC tools.
• Familiarity with observability and operational best practices for production systems.
• Experience supporting real-time personalization, recommendation, or analytics systems.
• Familiarity with feature stores, event-driven architectures, and real-time 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.
• Strong collaboration skills across engineering and data teams.

Tools and technologies

• Kafka, Kinesis, Spark, Flink
• Python, Java, Scala, Go, C++
• AWS, containers, Kubernetes, IaC tools
• Feature stores

Location and compensation

Glendale, CA (onsite). Salary range: USD 144,100 - 193,200 per year.

Education

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