Principal Machine Learning Engineer

Jobtailor

California (MO)

On-site

USD 180,000 - 240,000

Full time

3 days ago

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Job summary

Disney is seeking a senior ML platform architect to own the end-to-end architecture for a multi-brand ML platform. You will craft architecture documents and drive implementation to ensure scalable, reliable solutions aligned with standards.

You will lead how data pipelines, orchestration, and feature stores evolve, champion reliability, and mentor engineers while connecting platform investments to measurable guest experience enhancements.

Qualifications

Bachelor’s degree in computer science, information systems, statistics, math, or equivalent.
10+ years building and operating ML engineering systems in production.
Deep expertise in data science, deep learning, and statistical methods.
Experience owning architecture across a major platform or product domain.
Proven ability to drive ML platform improvements and personalization quality.
Experience designing backend microservices for large-scale distributed systems.

Responsibilities

Define and own end-to-end architecture of the ML platform across multiple brands.
Author architecture documents, drive reviews, and oversee implementation.
Identify, scope, and prioritize ML workstreams across the portfolio.
Drive data pipelines, workflow orchestration, and feature stores for ML lifecycle.
Champion reliability, observability, and operational excellence.
Mentor senior engineers and influence governance across groups.
Lead incident response and drive reliability programs.
Engage with cross-organizational communities to align standards.

Skills

Machine Learning Engineering

Data Science Expertise

Cloud Infrastructure (AWS)

MLOps Platforms (MLflow, SageMaker, V

Education

Bachelor’s degree in Computer Science or related

Tools

Databricks

Spark

Kinesis

Kafka

MLflow

SageMaker

Vertex AI

Job description

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.

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