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

The hiring range for this position in New York, NY is $148,700 - $199,400 per year and in Glendale, CA is $141,900 - $190,300. The base pay actually offered will take into account internal equity and also may vary depending on the candidate's geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.