Lead Machine Learning Engineer

$180,200 - $241,600/Yr

Disney - Seattle, WA

Full-time - Mid Level

Seattle, WA

Motion Picture and Sound Recording Industries

About the position

The Machine Learning Engineer position at Disney Entertainment & ESPN Technology focuses on leading the development and optimization of recommendation and personalization algorithms. This role involves working on event-driven ML pipelines and enhancing the ML infrastructure to support various product functions, including personalization, fraud prevention, and subscriber growth. The engineer will collaborate with cross-functional teams to drive innovation and improve data collection and analysis processes.

Responsibilities

• Utilize cutting edge machine learning methods to deploy and develop algorithms for personalization and recommendation systems.
• Maintain algorithms deployed to production and explain methodologies to technical and non-technical teams.
• Develop and maintain ETL pipelines using orchestration tools such as Airflow and Jenkins.
• Deploy scalable streaming and batch data pipelines to support petabyte scale datasets.
• Establish new algorithm development, testing, and deployment standards.
• Identify and define new personalization opportunities in collaboration with product and business stakeholders.

Requirements

• 7+ years of relevant experience developing machine learning models and performing large-scale data analysis.
• 7+ years of experience writing production-level, scalable code (e.g. Python, Scala).
• 5+ years of experience developing algorithms for deployment to production systems.
• In-depth understanding of modern machine learning methods and their mathematical underpinnings.
• Experience deploying and maintaining pipelines (AWS, Docker, Airflow) and engineering big-data solutions using technologies like Databricks, S3, and Spark.
• Strong written and verbal communication skills.

Nice-to-haves

• MS or PhD in statistics, math, computer science, or related quantitative field.
• Production experience with developing content recommendation algorithms at scale.
• Experience building and deploying full stack ML pipelines: data extraction, data mining, model training, feature development, testing, and deployment.
• Familiarity with metadata management, data lineage, and principles of data governance.
• Experience loading and querying cloud-hosted databases.
• Building streaming data pipelines using Kafka, Spark, or Flink.

Benefits

• Medical benefits
• Financial benefits
• Bonus and/or long-term incentive units based on performance