Lead Machine Learning Engineer - News

Disney Disney · Media · Glendale, CA +1

Lead Machine Learning Engineer for the News ML team, responsible for building data pipelines and ML platforms for personalized content delivery across various Disney platforms. The role involves driving infrastructure for scalable learning, inference, and monitoring, and collaborating with product and engineering teams to shape algorithmic innovation.

What you'd actually do

  1. Own complex technical initiatives end-to-end, from technical design through production deployment and operational excellence
  2. Design and develop infrastructure supporting the full cycle of machine learning, including data pipelines and workflow orchestration, data discovery and quality tools, and feature libraries
  3. Drive data and ML-driven solutions for diverse engineering use cases such as recommendation systems, object detection, autogenerated tagging solutions, RAGs
  4. Partner with product, editorial, and engineering stakeholders to translate business requirements into robust technical solutions
  5. Strategically prioritize initiatives and technical workstreams to deliver the highest-impact and most time-sensitive outcomes, while proactively identifying, communicating, and mitigating risks to ensure successful execution

Skills

Required

  • software engineering experience
  • developing and deploying machine learning systems in production
  • data science
  • deep learning algorithms
  • statistical methods
  • data collection
  • data analysis
  • feature engineering
  • batch training
  • low-latency online serving
  • backend microservices
  • large-scale distributed systems
  • REST
  • cloud infrastructure
  • AWS
  • Spark
  • ML pipelines
  • big data technologies
  • Databricks
  • Kinesis
  • Kafka
  • leadership
  • coaching
  • mentoring
  • observability tools
  • metrics
  • logging
  • monitoring
  • Datadog
  • Agile/Scrum

Nice to have

  • Personalize
  • ABC News
  • Good Morning America
  • local news stations

What the JD emphasized

  • 5+ years of hands-on experience developing and deploying machine learning systems in production
  • Expertise in data science, deep learning algorithms, or statistical methods to solve real-world engineering problems
  • Comfortable operating at all levels of the predictive stack, including data collection, data analysis, feature engineering, batch training and low-latency online serving

Other signals

  • personalized experiences
  • real-time content personalization
  • targeted distribution
  • recommendation algorithms
  • algorithmic innovation