Sr Machine Learning Engineer

The Walt Disney Company • New York, NY

1d • Onsite

About The Position

This is not a remote role. You must be in the area or willing to relocate. Department/Group Overview: The cross-media measurement and advanced analytics organization is responsible for data strategy & management, cross-platform content measurement, Content marketing measurement, and linear and digital inventory forecasting. The team provides advanced analytics and actionable insights related to Disney entertainment's content, monetization, and audience development. The Data and Analytics Operations team is part of the Cross-Media Measurement and Advanced Analytics (CMAA) organization. Reporting to the Executive Director of Data and Analytics Operations, this team leverages advanced machine learning techniques to deliver a robust suite of analytics solutions. Their portfolio includes descriptive, predictive, and prescriptive analytics, underpinned by strong data management practices and an interoperability layer. These capabilities are structured to support a range of business goals, such as content production, marketing and monetization. Job Summary: The Senior Machine Learning Engineer serves as an individual contributor responsible for leading end-to-end development of machine learning solutions, from data and feature design through model deployment and monitoring. This role applies machine learning techniques in code (e.g., supervised/unsupervised learning, classification/regression, clustering, and deep learning where appropriate) to develop systems that predict outcomes at scale for identity, audience, and cross-platform measurement use cases. The position includes building scalable ML pipelines and the data foundations required to capture, manage, store, and utilize large-scale structured and unstructured datasets, ensuring data integrity and interoperability across systems.

Requirements

• Must have at least 5 years of professional experience in machine learning engineering delivering production-grade models and ML pipelines at scale
• Must have advanced coding skills in Python and SQL; strong software-engineering best practices (version control, CI/CD, unit testing, code reviews)
• Must have demonstrated experience applying ML techniques in code to develop predictive systems (supervised/unsupervised learning; deep learning where appropriate)
• Hands-on experience with cloud-native data platforms and distributed processing (Snowflake/Databricks/Spark/BigQuery) and orchestration (Airflow/Dagster)
• Experience with containerization and production deployment patterns (Docker/Kubernetes) and operational monitoring
• Bachelor's degree in a relevant technical or science field (e.g. computer science, data science, mathematics, or a related discipline)

Nice To Haves

• 5+ years total experience, with hands-on work in media, advertising technology, or cross-platform audience measurement
• Production experience with deep-learning, genAI, or retrieval-augmented systems (PyTorch, vector databases) and real-time data pipelines (Kafka, Pub/Sub, Kinesis)
• Familiarity with modern MLOps stacks (e.g., MLflow, Kubeflow, Vertex AI, SageMaker) and model-governance practices (metadata, lineage, drift detection)
• Certifications such as Google Professional Machine Learning Engineer, AWS Certified Machine Learning – Specialty, or equivalent cloud/data credentials
• Contributions to open-source ML or data-engineering projects, conference presentations, or peer-reviewed publications
• Master’s degree or PhD in a relevant field (e.g., Applied Math, Computer Science, Computational Science, Operation Research, Data Science)

Responsibilities

• Develop, train, and deploy ML models for audience identity, look-alike modeling, and cross-platform measurement (including deep learning where appropriate); translate algorithms and technical specs into clean, testable Python/SQL code; containerize workloads via Docker/Kubernetes.
• Design and own scalable ML data and feature pipelines using orchestration tools (Airflow/Dagster) to capture, validate, and deliver cross-media datasets across distributed cloud and/or platform environments.
• Feature engineering & data preparation: develop reusable feature sets, manage metadata/lineage, and optimize storage/performance in Snowflake or Databricks to support training and inference.
• MLOps & monitoring: implement CI/CD, model versioning/registry patterns, automated evaluation, and drift detection; build dashboards/alerts to ensure model reliability, reproducibility, and data quality in production.
• Stakeholder collaboration & experimentation: lead offline/online experiment design, interpret results, and translate findings into actionable product enhancements for analytics, product, and engineering teams.
• Data privacy & governance compliance: apply GDPR/CCPA principles, enforce PII safeguards, and contribute to documentation and audit readiness.
• Team enablement: mentor junior engineers through code reviews and design reviews; share best practices and reusable tooling.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level