Senior Machine Learning Engineer

Posted today

disney entertainment sportsGlendale (CA)

SENIORITY

Senior

SALARY

$144,100 - $193,200 per year

About the role

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

Responsibilities:

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

Requirements:

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
TechnologiesKafka, Kinesis
Spark, FlinkPythonJava, Scala, Go, C++AWSContainers, Kubernetes, IaC tools
Feature stores
EducationBachelor's or Master's degree in Computer Science, Data Engineering, Machine Learning, or a related field
Location and Salary

Location: Glendale, CA (onsite)

Compensation: USD 144,100 - 193,200 per year

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