About the Role & Team

Disney Entertainment’s (DE) Cross-Media Measurement and Advanced Analytics organization drives data strategy, cross-platform content measurement, marketing effectiveness, and inventory forecasting. The team delivers advanced analytics and actionable insights to support content, monetization, and audience development.

The Data and Analytics Operations team, part of this organization, applies advanced machine learning techniques to deliver a suite of analytics solutions—descriptive, predictive, and prescriptive—built on strong data management and interoperability practices. These capabilities enable business goals across content production, marketing, and monetization.

What You Will Do

Reporting to the Executive Director of Data and Analytics Operations, the Machine Learning Engineer II is an individual contributor responsible for designing, developing, and deploying machine learning models. This hands-on role focuses on building scalable ML pipelines, writing production code, and operationalizing models that leverage cross-platform audience data. You will collaborate closely with product managers, data scientists, and platform engineers to construct, optimize, and deploy analytics systems.

Responsibilities

• Data Pipeline Development: Build and optimize scalable pipelines using orchestration tools (Airflow/Dagster) to ingest, transform, and deliver cross-media datasets.
• Feature Engineering & Data Prep: Create high-quality features from petabyte-scale data, manage metadata, and optimize storage/performance in Snowflake or Databricks.
• Model Development: Design, train, and deploy ML models for audience identity, look-alike modeling, and cross-platform measurement; write clean, testable Python/SQL code and containerize workloads via Docker/Kubernetes.
• MLOps & Monitoring: Implement CI/CD, model versioning, automated testing, and drift detection; build dashboards and alerts to ensure reliability and data quality in production.
• Collaboration & Experimentation: Gather requirements, run experiments, and translate findings into actionable product enhancements for analytics, product, and editorial teams.
• Compliance: Apply GDPR/CCPA principles, enforce PII safeguards, and maintain documentation for audit readiness.
• Continuous Learning: Research emerging ML techniques, share best practices, and mentor junior engineers through code reviews and knowledge-sharing sessions.
   

Required Qualifications & Skills

• 3+ years of professional experience in machine learning engineering, delivering production-grade models or pipelines at scale.
• Advanced coding skills in Python and SQL; familiarity with a strongly typed language and software engineering best practices (version control, CI/CD, unit testing).
• Hands-on experience with cloud-native data platforms and distributed frameworks (BigQuery, Snowflake, Databricks) and orchestration tools (Airflow, Dagster).
• Proven ability to build end-to-end ML workflows: feature engineering, model training, containerization, monitoring, and performance tuning.
• Working knowledge of data privacy standards (GDPR, CCPA) and experience applying them to identity or audience datasets.
• Strong collaboration skills with cross-functional teams and ability to translate technical findings into business insights.
   

Preferred Qualifications

• 3+ years of experience, ideally in media, ad tech, or cross-platform audience measurement.
• Experience with deep learning, generative AI, or retrieval-augmented systems (PyTorch, vector databases) and real-time pipelines (Kafka, Pub/Sub, Kinesis).
• Familiarity with modern MLOps stacks (MLflow, Kubeflow, Vertex AI, SageMaker) and model governance practices.
• Relevant certifications (Google ML Engineer, AWS ML Specialty, or equivalent).
• Contributions to open-source projects, conference presentations, or peer-reviewed publications.
   

Required Education

• Bachelor’s degree in Computer Science, Data Science, Mathematics, or related technical field.
   

Preferred Education

• Master’s or PhD in Applied Math, Computer Science, Computational Science, Operations Research, or Data Science.

The hiring range for this position in New York, NY is $123,000.00 - $165,000.00 per year. 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.