Sr. Manager, Machine Learning Engineering

The Walt Disney Company • New York, NY

1d • Onsite

About The Position

The Senior Manager, Machine Learning Engineering will lead a team responsible for building and operating production ML systems that deliver predictive outcomes at scale for cross-media measurement, identity resolution, and audience development. This role requires deep hands-on understanding of applied machine learning and ML engineering and is accountable for ensuring that machine learning techniques are applied in code (e.g., supervised / unsupervised learning, deep learning/neural networks where appropriate, and related modeling frameworks) and operationalized reliably through robust MLOps practices. The position also includes ownership of key data and pipeline foundations required to capture, manage, store, and utilize large-scale structured and unstructured data from internal and external sources to support model training and inference, while enforcing privacy, governance, and audit readiness.

Requirements

• Must have 10+ years of experience in machine learning engineering and/or applied ML roles delivering production ML systems (models + pipelines + monitoring)
• Must have 4+ years in a technical leadership capacity, including people leadership and/or strong delivery ownership in ML environment
• Must have knowledge of data privacy regulations (GDPR, CCPA) and implementing privacy-aware data and modeling practices
• Proven experience applying machine learning techniques in code to develop predictive systems at scale (including deep learning where appropriate)
• Strong proficiency in Python and SQL; software engineering best practices (version control, CI/CD, automated testing)
• Hands-on experience with cloud-native data platforms and distributed processing (Snowflake/Databricks/BigQuery/Spark) and orchestration (Airflow/Dagster)
• Bachelor's degree in a relevant technical or science field (e.g. computer science, data science, mathematics, or a related discipline)

Nice To Haves

• 12+ years total experience, with hands-on work in media, advertising technology, or cross-platform audience measurement
• 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)
• Strong understanding 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
• Experience in media/ad tech, identity graphs, audience measurement, or interoperability layers
• Experience with modern MLOps platforms (MLflow, Kubeflow, Vertex AI, SageMaker) and model governance practices
• Master’s degree or PhD in a relevant field (e.g., Applied Math, Computer Science, Computational Science, Operation Research, Data Science)

Responsibilities

• 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.
• 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)
• 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.
• Lead a team of ML engineers: hiring, onboarding, coaching, performance management, code/design reviews, and career development; set technical direction and quality standards.
• Lead cross-organization decision-making: align stakeholders, define success metrics, and drive complex trade-offs to deliver durable, scalable ML solutions.
• Stakeholder collaboration & roadmap execution: partner with product, analytics, engineering, and governance stakeholders to translate business needs into technical requirements, success metrics, and delivery plans.
• Champion data privacy, governance, and compliance: enforce GDPR/CCPA principles, PII safeguards, documentation, and audit readiness across ML workflows.

What This Job Offers

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