> Markdown version of [/jobs/ext/4380933-sr-machine-learning-engineer](https://www.wearedevelopers.com/jobs/ext/4380933-sr-machine-learning-engineer). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md).

---

# Sr Machine Learning Engineer

- **Company:** The Walt Disney Company
- **Location:** Santa Monica, CA, United States
- **Experience:** Expert
- **Salary:** $144,100.0 - $193,200.0
- **Contract:** Permanent contract
- **Skills:** Training Data, A/B Testing, Application Programming Interfaces (APIs), Amazon Web Services, Automated Storage and Retrieval Systems, Information Systems, Data Validation, Data Systems, Distributed Computing Environment, Python (Programming Language), Search Algorithms, Machine Learning, Azure Machine Learning, Data Logging, Feature Store, Cloud Platform System, Feature Engineering, Delivery Pipeline, Apache Spark, Backend, Event Driven Architecture, Information Technology, Low Latency, Data Analytics, Feature Selection, Machine Learning Operations, Databricks
- **Published:** October 28, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=f7b3f1f3c59f281b

## About the Role

* 5+ years of experience developing and deploying machine learning models in production environments.
 * Production-quality Python; strong working knowledge of SQL.
 * Experience building or operating search ranking, recommendation, or similar retrieval systems at scale.
 * Experience deploying and operating ML systems in cloud environments (AWS); comfort with distributed data processing at scale (e.g., Spark) and production observability practices.
 * Strong written and verbal communication skills; comfort explaining algorithmic tradeoffs to non-technical partners, * 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.

Required Education

 * Bachelor's degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience

## Description

The Search Engineering team owns the backend systems that power search across Disney+ and Hulu: from the viewer-facing API, search index and ranking models, to the pipelines, configuration, and tooling that power them. We determine what millions of users find when they search for content.

As a Senior Machine Learning Engineer, you will be expected to lead search algorithm research, development, and productionization for existing and new product areas. You will identify opportunities to advance search quality on your own initiatives.

You will collaborate with Product and Engineering Leadership to align on search quality goals; with Data Analytics to ensure offline and online evaluation methodology is sound; and with Software Engineers, Data Engineers, and other ML engineers on the team, including those focused on ML platform work, to design systems that are reliable and maintainable at scale

Responsibilities and Duties of the Role:

 * Contribute to the ongoing development of the team's search ranking system: iterating on model architecture, feature selection, training signal design, and integration of user context signals to improve ranking quality across personalized and nonpersonalized traffic.
 * Evaluate model performance against offline and online metrics. Collaborate on hypothesis formulation, experimental design, and the implementation of A/B tests.
 * Maintain production search ranking systems and serve as the technical point of contact in collaboration with Product and Data Analytics.
 * Work with Data Engineers on feature engineering workflows that produce the training data and behavioral signals that feed search ranking models
 * Build and operate ML inference systems for search ranking, meeting the latency and availability requirements of production search.
 * Own training pipeline code and model deployment workflows for your models end-to-end, from training data through artifact deployment, using the team's Databricks-based infrastructure.
 * Instrument production search ML systems with metrics, logging, and alerting; participate in on-call rotation for search ML components.