Disney Entertainment & Sports LLC (ESPN) is expanding large-scale data infrastructure and real-time processing capabilities to support next-generation personalization and live sports experiences. This role focuses on building and operating distributed data and machine learning infrastructure for high-throughput, low-latency workflows and real-time ML use cases.
Role Overview
As a Senior Machine Learning Engineer, you will design, build, and maintain batch and streaming data pipelines, develop real-time feature computation systems, and support production ML workflows. The position also involves ensuring operational reliability through monitoring, alerting, and incident response, while partnering with platform and SRE teams to improve availability, performance, and cost efficiency.
Key Responsibilities
- 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.
- Meet latency, reliability, and data quality requirements for downstream ML and product teams.
- Develop and operate systems for real-time feature computation and delivery to online ML services.
- Use feature stores and event-driven architectures to support consistency between offline and online data.
- 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.
- 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.
- Instrument data and ML infrastructure with metrics, logging, and alerting for production operations.
- Participate in on-call rotations and incident response for data and ML platforms.
- Identify and remediate data pipeline failures, performance regressions, and operational risks.
- 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 while following established platform standards.
- Make incremental improvements over time in line with platform expectations.
Required Skills and Experience
- Experience building and operating large-scale data or ML systems in production.
- Strong foundations in distributed systems and data processing architectures.
- Hands-on experience with streaming and batch technologies such as Kafka, Kinesis, Spark, and Flink (or equivalent).
- Proficiency in Python, with working knowledge of Java, Scala, Go, or C++.
- Experience operating systems in cloud-native environments, including AWS, containers, Kubernetes, and IaC tools.
- Familiarity with observability and operational best practices for production systems.
- Ability to collaborate effectively across engineering and data teams.
- 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.
- Minimum 5+ years of industry experience building data-intensive or ML-adjacent systems in production.
Relevant Technologies
- Kafka, Kinesis, Spark, Flink
- Python, Java, Scala, Go, C++
- AWS, containers, Kubernetes, IaC tools
- Feature stores
Location
Glendale, CA (onsite)
Compensation
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.
