Lead Machine Learning Engineer

This is not a remote role. You must be in the local area or willing to relocate.

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 Lead Machine Learning Engineer is a senior individual contributor who provides technical leadership for complex machine learning systems and the data foundations required to operate them. This role applies machine learning techniques in code (e.g., supervised/unsupervised learning, deep learning/neural networks where appropriate, and advanced modeling approaches) to build predictive systems at scale for identity, audience, and cross-platform measurement. The position also leads architecture and standards for ML pipelines that capture, manage, store, and utilize large-scale structured and unstructured data, ensuring data integrity, interoperability, and reliability across production environments.

• Lead development, training, and deployment of advanced ML models for identity resolution, look-alike modeling, and cross-platform measurement; translate algorithms into production-quality code; optimize for scale and performance.
• Architect scalable ML platforms and reusable components (training/inference pipelines, feature/label foundations, model serving patterns) that operate across distributed cloud and platform environments
• Lead data and feature foundations: define data contracts, metadata/lineage expectations, and automated quality controls to maintain data integrity across structured/unstructured sources in Snowflake/Databricks.
• MLOps & reliability: establish CI/CD patterns, model versioning/registry practices, automated evaluation, drift detection, monitoring dashboards/alerts, and operational playbooks for sustained production health.
• Cross-functional technical leadership: drive design reviews, clarify technical requirements, and lead multi-quarter initiatives with product, analytics, and platform engineering stakeholders.
• Mentorship & enablement: mentor engineers through code/design reviews; build shared libraries and best practices to improve team velocity and quality.
• Privacy, governance & compliance: ensure privacy-by-design practices, PII safeguards, documentation, and audit readiness across ML workflows (GDPR/CCPA).

• Must have 7+ years of professional experience delivering production ML systems (models + pipelines + monitoring) at scale
• Must have advanced coding skills in Python and SQL; strong software engineering discipline (testing, CI/CD, code review, design documentation)
• Must have demonstrated experience applying ML techniques in code to develop predictive systems at scale (including deep learning where appropriate)
• Must have hands-on expertise with cloud-native data platforms and distributed compute (Snowflake/Databricks/Spark/Big Query) and container orchestration (Docker/Kubernetes)
• Proven ability to lead technical initiatives across teams and influence architecture and standards

• 8+ years total experience, with hands-on work in media, advertising technology, or cross-platform audience measurement
• Strong 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, Sage Maker) 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
• Experi…