Machine Learning Engineer - Search

Overview

Welcome to Warner Bros. Discovery… the stuff dreams are made of.

When we say, “the stuff dreams are made of,” we’re not just referring to the world of wizards, dragons and superheroes, or even to the wonders of Planet Earth. Behind WBD’s vast portfolio of iconic content and beloved brands, are the storytellers bringing our characters to life, the creators bringing them to your living rooms and the dreamers creating what’s next…

From brilliant creatives, to technology trailblazers, across the globe, WBD offers career defining opportunities, thoughtfully curated benefits, and the tools to explore and grow into your best selves. Here you are supported, here you are celebrated, here you can thrive.

Max is where storytelling takes center stage and where creatives find a home with the support and resources to do their best work, no matter the genre or format. It is where our exclusive Max Originals and iconic entertainment brands thrive, with HBO, HBO Max, Warner Bros., DC, Turner Classic Movies, Cartoon Network and more delivering the greatest array of series, movies, and specials for audiences of all ages.

HBO Max originally launched in the United States in May 2020 and introduced a lower priced, advertising-supported tier in June 2021. It rolled out globally across Latin America and the Caribbean in 2021, followed by its first European launches in the Nordics and Spain later that year. In May 2023, Warner Bros. Discovery introduced Max, an enhanced streaming platform, in the U.S. HBO Max is currently available in 46 countries and there are plans for the continued global expansion of Max in 2024.

Staff MLE, Personalization, Content Discovery

Location:
Seattle, San Francisco Bay Area, New York

Every great story has a new beginning.

We are looking for a passionate Machine Learning Engineer to build and scale the DTC personalization models and algorithms for our new global streaming app, Max, as well as any future DTC streaming apps. You are excited about working in an environment that fosters innovation via prototyping, development, experimentation, and productionization. You will bring the right balance between rapid feature iteration and building a common set of platforms and tools to move quickly in the future.

In your role, you will be working alongside a team of passionate machine learning engineers and applied Machine Learning researchers to build and contribute to architecting a system that serves millions of users worldwide.

• Architect, build and scale a recommendation system that powers the state-of-the-art personalization experience to users across Max, HBO, Discovery+ and other WBD offerings
• Collaborate with ML/Ops engineers to develop and improve core components, infrastructure and architecture to train, deploy and serve models at scale
• Collaborate with other data scientists, engineers, product teams and other key stakeholders and drive ML projects from conception to completion
• Author, test, review, and optimize production-level code in Python, Go and Java while executing best practices in version control and code integration
• Use and build upon open-source cloud computing technologies
• Participate and support engineering leaders in strategic planning and demonstrate good judgment in setting and delivering against strategic goals for the team
• Motivate, inspire and create a culture of experimentation and data-driven innovation while constantly striving to be an advocate for doing what is right for our customers

• 8+ years of industry experience, with 4+ years as tech lead experience (preferred)
• Deep practical knowledge of modern machine learning lifecycle.
• Deep practical knowledge of large-scale recommender systems, or large-scale ML ranking/retrieval/targeting systems and familiarity with A/B Tests and hypothesis testing is preferred
• Experience with design, implementation, and performance tuning of ML models.
• 5+ years of programming experience in at least one of the following:
  Python/Java/Go with ability to rapidly prototype ideas and refine towards production.
• Deep practical knowledge of large-scale recommender models and systems, or large-scale ML…