Lead Machine Learning Engineer

The Walt Disney Company • New York, NY

9h • Onsite

About The Position

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.

Requirements

• 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/BigQuery) and container orchestration (Docker/Kubernetes)
• Proven ability to lead technical initiatives across teams and influence architecture and standards
• Bachelor's degree in a relevant technical or science field (e.g. computer science, data science, mathematics, or a related discipline)

Nice To Haves

• 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, 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 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).

What This Job Offers

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