Senior Machine Learning Engineer

Machine Learning Engineer

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