Senior Machine Learning Engineer

Join ESPN to build and run the distributed data and machine learning infrastructure behind real-time sports personalization and live experiences.

• Build and maintain high-throughput batch and streaming data pipelines for ML, analytics, and real-time decisioning
• Implement data ingestion, enrichment, aggregation, and transformation using modern distributed data frameworks
• Deliver pipelines that meet latency, reliability, and data quality needs for downstream ML and product teams
• Develop and operate systems for real-time feature computation and delivery used by online ML services
• Partner with applied ML and data science teams to enable production ML workflows through reliable data systems
• Work with feature stores and event-driven architectures to keep offline and online data consistent
• Improve data freshness
  , schema evolution
  , and backward compatibility for 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
• Collaborate with platform and SRE teams to improve 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
• Diagnose and remediate data pipeline failures, performance regressions, and operational risks
• Participate in design reviews, code reviews, and technical discussions
• Follow established platform standards and contribute incremental improvements over time

• 5+ years of industry experience building data-intensive or ML-adjacent systems in production
• 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 equivalent
• Proficiency in Python and working knowledge of Java, Scala, Go, or C++
• Experience operating in cloud-native environments including AWS
  , containers,
  Kubernetes
  , and IaC tools
• Familiarity with observability and operational best practices for production systems
• Strong collaboration skills 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 including 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

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

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

• Location: Glendale, CA (onsite)
• Compensation: USD 144, per year