Sr. Manager, Machine Learning Engineering

Disney Disney · Media · New York, NY +1

Senior Manager, Machine Learning Engineering to lead a team building and operating production ML systems for cross-media measurement, identity resolution, and audience development. Requires deep applied ML and ML engineering expertise, robust MLOps, and ownership of data pipelines for large-scale data. Must enforce privacy and governance.

What you'd actually do

  1. Lead delivery of machine learning systems for identity, audience modeling, and cross-platform measurement; ensure ML techniques are applied in code and deployed as scalable, production-grade services and/or pipelines.
  2. Oversee ML data and feature foundations: design and maintain pipelines that capture, transform, and deliver structured and unstructured cross-media datasets from internal/external sources; ensure interoperability and data integrity across platforms (Airflow/Dagster; Snowflake/Databricks)
  3. MLOps & monitoring ownership: implement and standardize CI/CD, model versioning/registry practices, automated evaluation/testing, drift detection, dashboards/alerts, and operational runbooks to ensure reliability and reproducibility.
  4. Lead a team of ML engineers: hiring, onboarding, coaching, performance management, code/design reviews, and career development; set technical direction and quality standards.
  5. Lead cross-organization decision-making: align stakeholders, define success metrics, and drive complex trade-offs to deliver durable, scalable ML solutions.

Skills

Required

  • Python
  • SQL
  • software engineering best practices (version control, CI/CD, automated testing)
  • cloud-native data platforms and distributed processing (Snowflake/Databricks/BigQuery/Spark)
  • orchestration (Airflow/Dagster)
  • data privacy regulations (GDPR, CCPA)
  • MLOps stacks
  • model governance practices

Nice to have

  • media, advertising technology, or cross-platform audience measurement
  • MLflow, Kubeflow, Vertex AI, SageMaker
  • 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
  • identity graphs, audience measurement, or interoperability layers
  • Master’s degree or PhD

What the JD emphasized

  • Flagship production ownership experience with ML, deep-learning, genAI, or retrieval-augmented systems (PyTorch, vector databases) and real-time data pipelines (Kafka, Pub/Sub, Kinesis)
  • 10+ years of experience in machine learning engineering and/or applied ML roles delivering production ML systems (models + pipelines + monitoring)
  • 4+ years in a technical leadership capacity, including people leadership and/or strong delivery ownership in ML environment
  • knowledge of data privacy regulations (GDPR, CCPA) and implementing privacy-aware data and modeling practices

Other signals

  • production ML systems
  • predictive outcomes at scale
  • MLOps practices
  • large-scale structured and unstructured data
  • identity resolution
  • audience development