Lead Machine Learning Engineer

Job Summary:

We are looking for a Lead Machine Learning Engineer to help us ideate, develop, iterate on, and productionize personalization algorithms across the recommendation stack. This includes our core ranking algorithms, content and user understanding models and graphs, as well as candidate retrieval and post-ranking systems.

There is more than one way to be a great fit for this role. You might be a strong applied scientist with sharp intuition for recommendation approaches, evaluation methodology, and how data, features, and objectives shape model behavior. You might be a strong end-to-end ML engineer who can take ideas to production at scale and keep systems healthy, maintainable, and easy to iterate on. Ideally, you bring a blend of both: someone who generates ideas of their own, helps other applied scientists bring theirs to life, and can jump in from either the science or the engineering side when something needs attention.

This is also an opportunity to work at the frontier. We are especially excited about candidates with strong relevant experience (RecSys, ML, AI/LLM) who can help bridge where recommendation systems are today and where the field is heading, applying modern AI techniques not only to improve recommendations themselves, but to improve how we build, evaluate, and iterate on our systems.

In this role, you will help drive the vision and innovation behind Disney's personalization systems, with the goal of delighting our users through great content recommendations, improving customer satisfaction, and deepening our understanding of both content and users.

Responsibilities:

• Algorithm development: Ideate, develop, iterate on, and productionize personalization algorithms, including core ranking, content and user understanding models and graphs, candidate retrieval, and post-ranking systems.
• AI and LLM innovation: Apply modern AI and LLM techniques to recommendation systems, including using them to generate and improve recommendations, strengthen system evaluation, and accelerate how we build and improve our models.
• Applied science: Contribute ideas and insight on recommendation approaches, evaluation methodology, and how we define data, features, and objectives for our models, and help other scientists on the team shape and productionize their ideas.
• Vision and roadmap: Help drive the technical vision and innovation agenda for personalization, identifying high-impact opportunities and shaping how the team approaches them.
• Experimentation and evaluation: Design and run rigorous offline and online experiments, and contribute to improving our evaluation systems and methodology.
• Collaboration: Work closely within the team and across Engineering, Product, and Data partners, communicating methodologies clearly to technical and non-technical audiences and managing stakeholder expectations.
• ML engineering: Build production-worthy, maintainable systems that are easy to iterate on, uphold strong standards for development, testing, and deployment, and jump in to support when production issues arise.

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Basic Qualifications

• 7+ years of experience developing machine learning models and deploying them to production systems
• Strong background in applied ML science, end-to-end ML engineering, or ideally a blend of both, with experience in recommendation systems modeling
• Hands-on experience with AI and LLM techniques and a solid understanding of the modern AI landscape
• Proficiency with tools and frameworks such as PyTorch, TensorFlow, Databricks, Spark, and SQL
• In-depth understanding of modern machine learning methods, models, and their mathematical underpinnings
• Strong written and verbal communication skills
• A collaborative, personable working style; works well within the team and across teams rather than operating in isolation

Preferred Qualifications

• PhD in computer science, statistics, math, or a related quantitative field
• Publications or papers in machine learning or AI, especially in recommender systems
• Production experience developing content recommendation algorithms at scale
• Experience with reinforcement learning or related sequential decision-making approaches
• Experience with evaluation methodology for recommendation systems, including offline evaluation and A/B experimentation

Required Education

• BS or MS in Computer Science, Engineering, or a related field

The hiring range for this position in San Francisco, CA is $187,900.00 - $252,000.00 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.