# Senior Machine Learning Engineer - ESPN

**Company**: Disney Entertainment & ESPN Technology
**Location**: Glendale, CA
**Experience**: senior
**Job type**: full-time
**Salary**: $144,100.00 - $193,200.00 per year
**Category**: Engineering
**Industry**: Technology

**Apply**: https://disney.wd5.myworkdayjobs.com/en-US/disneycareer/job/Glendale-CA-USA/Senior-Machine-Learning-Engineer_10150610?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply
**Canonical**: https://yubhub.co/jobs/job_3565053d-861

## Description

Disney Entertainment & ESPN Technology is seeking a Senior Machine Learning Engineer to work on building and operating large-scale data infrastructure and real-time processing platforms that power next-generation personalization and live sports experiences.

The ideal candidate will have experience building and operating distributed data and ML infrastructure that supports high-throughput, low-latency data processing and real-time ML use cases. They 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.

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.

Required Education, Experience/Skills/Training:

- Basic Qualification:

- 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.

The hiring range for this position in New York, NY is $150,900.00 - $202,400.00 per year, in Glendale, CA is $144,100.00 - $193,200.00 per year, and in San Francisco, CA $157,800.00 - $211,600.00 per year.

## Skills

### Required
- Python
- Java
- Scala
- Go
- C++
- Kafka
- Kinesis
- Spark
- Flink
- AWS
- Kubernetes

### Nice to have
- feature stores
- event-driven architectures
- real-time ML pipelines
- inference pipelines
- data validation
- model lifecycle tooling

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Source: [Apply at disney.wd5.myworkdayjobs.com](https://disney.wd5.myworkdayjobs.com/en-US/disneycareer/job/Glendale-CA-USA/Senior-Machine-Learning-Engineer_10150610?utm_source=yubhub.co&utm_medium=jobs_feed&utm_campaign=apply)