Lead 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 local 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 organization (CMAA). 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 Principal Machine Learning Engineer is a highly experienced individual contributor responsible for defining technical direction and delivering the most complex machine learning systems across cross-media measurement and advanced analytics. This role applies machine learning techniques in code (e.g., deep learning/neural networks where appropriate, supervised/unsupervised learning, and advanced modeling frameworks) to build predictive systems at scale and to develop descriptive, predictive, and prescriptive algorithms for high-impact business use cases. The position also sets standards for the data architecture and engineering foundations required to capture, manage, store, and utilize structured and unstructured data across distributed cloud and platform environments, ensuring compatibility, operability, governance, and long-term reliability.

Requirements

• Must have 10+ years of professional experience delivering production ML systems at scale, including significant ownership of architecture and platform strategy
• Must have expert coding skills in Python and strong SQL; proven software engineering maturity (testing, CI/CD, design reviews, documentation)
• Demonstrated ability to apply ML techniques in code to build predictive systems at scale (including deep learning where appropriate)
• Proven ability to influence across teams and drive organizational standards for data/ML reliability, governance, and interoperability
• Deep experience with distributed compute/data platforms and performance optimization

Nice To Haves

• 12+ years total experience, with hands-on work in media, advertising technology, or cross-platform audience measurement
• Flagship production experience with 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

Responsibilities

• Define architecture and long-range technical strategy for ML systems and enabling platforms (training/inference orchestration, feature foundations, serving patterns, interoperability layers) across the measurement ecosystem.
• Lead development and productionization of next-generation ML approaches for identity resolution, audience modeling, and cross-platform measurement; translate advanced algorithms into scalable, production-quality implementations.
• Guide major data engineering and platform investments required for ML: distributed processing patterns, data routing/storage strategies, data integrity controls, and automation across internal/external data sources.
• Establish enterprise-grade MLOps, governance, and assurance processes: CI/CD standards, automated evaluation, model versioning/registry practices, drift detection, monitoring, and operational excellence.
• Lead cross-organization technical decision-making: align stakeholders, define success metrics, and drive complex trade-offs to deliver durable, scalable ML solutions.
• Coach and develop senior technical talent: mentor Staff/Senior engineers, set engineering standards, and build communities of practice.
• Champion privacy, security, and responsible AI: ensure privacy-by-design, PII safeguards, and audit readiness (GDPR/CCPA) for ML and data workflows.

What This Job Offers

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