Machine Learning Engineer

Define and own the end-to-end architecture of the N&E ML platform across ABC News, ABC Entertainment, National Geographic, Marvel, and Disney Studios Author architecture documents, drive them through review, and oversee implementation to ensure scalable, reliable solutions aligned with platform-wide standards Identify, scope, and prioritize impactful and time-sensitive ML workstreams across the N&E portfolio Break down and sequence complex initiatives, surface risks to leadership, and drive metrics-driven outcomes Drive the design and evolution of infrastructure supporting the full ML lifecycle, including data pipelines, workflow orchestration, feature stores, batch training, and low-latency online serving Champion reliability, quality, and operational excellence across the platform Identify and champion new ML, AI, and data engineering technologies, frameworks, and patterns Own and discuss production incidents during weekly meetings with leadership Drive reliability, observability, and continuous improvement processes Participate in Disney Entertainment & ESPN’s broader Machine Learning community Influence engineering standards, cross-organizational programs, and architectural governance Connect ML platform investments to measurable guest experience and business outcomes across all brands Mentor and elevate senior engineers while fostering technical rigor, ownership, and continuous learning Requirements Bachelor’s degree in computer science, Information Systems, Statistics, Math, or comparable field of study, and/or equivalent work experience 10+ years of experience building and operating ML engineering systems in production environments, with a track record of owning large, complex problem spaces Deep expertise in data science, deep learning algorithms, and statistical methods applied to real-world, large-scale engineering problems Demonstrated experience owning architecture across a significant platform or product domain, including authoring architecture documents, driving reviews, and leading implementation Proven ability to drive quantifiable improvements in ML platform capabilities, personalization quality, or recommendation system performance Experience designing and evolving backend microservices for large-scale distributed systems using REST Strong expertise with cloud infrastructure, preferably AWS (Step Functions, Lambda, Glue, SQS, SNS, Personalize) Deep hands-on experience with big data technologies such as Databricks, Spark, Kinesis, and Kafka Experience leading incident response for high priority incidents and driving reliability programs across a team or platform Active participation in cross-organizational engineering communities, standards-setting, and architectural governance Proven track record as a metrics-driven technical leader who connects engineering decisions to business outcomes Exceptional communication, influence, and collaboration skills — comfortable presenting to and aligning senior leadership and cross-functional stakeholders Experience working in Agile/Scrum environments with strong prioritization and stakeholder management skills Experience with agentic AI workflows and frameworks (e.g. LangGraph, AutoGen, CrewAI) and applying them to automate complex ML and data engineering tasks Familiarity with AI-assisted development tools such as Claude, Cursor, or GitHub Copilot Familiarity with prompt engineering, fine-tuning, and evaluation frameworks for large language models in production environments Experience with MLOps platforms and modern model lifecycle management tools (e.g. MLflow, SageMaker, Vertex AI) Core Competencies Demonstrates deep expertise in Machine Learning engineering, data science, and architecture, with a proven ability to drive quantifiable improvements in ML platform capabilities and connect engineering decisions to business outcomes. Strong leadership in mentoring engineers and fostering a culture of technical rigor and continuous improvement is essential. Highest-signal resume keywords Machine Learning Engineering Data Science Expertise Cloud Infrastructure (AWS) MLOps Platforms (MLflow, SageMaker, Vertex AI) Backend Microservices Design ATS Optimization Keywords Hard Skills Deep Learning Algorithms Statistical Methods Architecture Documentation Metrics-Driven Leadership Agile/Scrum Methodologies Prompt Engineering Fine-Tuning Frameworks Big Data Technologies (Databricks, Spark) Incident Response Leadership Workflow Orchestration Soft Skills Exceptional Communication Influence Collaboration Stakeholder Management Mentoring Industry Keywords Machine Learning Platform Operational Excellence Cross-Organizational Engineering Architectural Governance Personalization Quality Tools & Technologies AWS Step Functions AWS Lambda AWS Glue AWS SQS AWS SNS Databricks Spark Kinesis Kafka AI-Assisted Development Tools #J-18808-Ljbffr