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Über
ESPN is investing in large-scale data infrastructure and real-time processing platforms that power next-generation personalization and live sports experiences. As a Machine Learning Engineer, you will focus on building and operating distributed data and ML infrastructure that supports high-throughput, low-latency data processing and real-time 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 ML-adjacent services that operate reliably at scale. The role emphasizes hands-on engineering, strong fundamentals in distributed systems, and practical experience operating production data infrastructure. Responsibilities and Duties of the Role:
Build and maintain high-throughput batch and streaming data pipelines to support ML, analytics, and real-time 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.
Develop and operate systems that support real-time feature computation and delivery for online ML services.
Work with feature stores and event-driven architectures to ensure 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 to support 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.
Follow established platform standards and contribute incremental improvements over time
Required Education, Experience/Skills/Training:
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 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 cloud-native 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
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.
5+ years of industry experience building data-intensive or ML-adjacent systems in production
Bachelor's or Master's degree in Computer Science, Data Engineering, Machine Learning, or a related field
Sprachkenntnisse
- English
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