Senior Machine Learning Engineer
Role details
Job location
Tech stack
Job description
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:
- Large-Scale Data Processing & Streaming Systems
- 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.
- Real-Time Data & Feature Infrastructure
- 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.
- ML-Adjacent infrastructure & Platform Engineering
- 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.
- Reliability, Observability & Operations
- 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.
- Collaboration & Engineering Execution
- 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
Requirements
- 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
Preferred qualification:
- 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.
Experience with:
- 5+ years of industry experience building data-intensive or ML-adjacent systems in production
Required Education
- Bachelor's or Master's degree in Computer Science, Data Engineering, Machine Learning, or a related field
Benefits & conditions
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.