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Lead Machine Learning EngineerDisney Entertainment Television Careers • New York, New York, United States
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Lead Machine Learning Engineer

Disney Entertainment Television Careers
  • US
    New York, New York, United States
  • US
    New York, New York, United States
Postuler Maintenant

À propos

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 (CMMAA) . 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 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.
Responsibilities and Duties of the Role: - 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).
Required Education, Experience/Skills/Training:
Minimum Qualifications: - Must have strong production experience with deep-learning, genAI, or retrieval-augmented systems (PyTorch, vector databases) and real-time data pipelines (Kafka, Pub/Sub, Kinesis) - 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
Preferred Qualifications: - 8+ years total experience, with hands-on work in media, advertising technology, or cross-platform audience measurement - 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
Required Education: - Bachelor's degree in a relevant technical or science field (e.g. computer science, data science, mathematics, or a related discipline)
Preferred Education: - Master's degree or PhD in a relevant field (e.g., Applied Math, Computer Science, Computational Science, Operation Research, Data Science)
#DISNEYTECH
The hiring range for this position in New York City is $179,700.00 - $225,000.00 per year. The base pay actually offered will take into account internal equity and also may vary depending on the candidate's geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits, dependent on the level and position offered.
  • New York, New York, United States

Compétences linguistiques

  • English
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