Senior Machine Learning Engineer

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

Who We Are…

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.

At HBO Max, storytelling takes center stage. We’re one of the world’s most iconic entertainment brands — home to bold originals and unforgettable characters. While audiences binge award-winning content, breaking news, and sports around the clock, our teams stay busy at work creating what’s next in streaming. From Succession, Euphoria, and The Sopranos to global franchises like Game of Thrones and Harry Potter, our content sparks conversation and shapes culture.

HBO Max delivers boundary-pushing stories across genres and platforms, connecting millions of viewers across 90 countries globally— and we’re just getting started. We're home to the most talked about shows and movies, granting audiences access to the worlds of HBO, Harry Potter, DC, Warner Bros., ID, Adult Swim, A24, and more. Turn your streaming obsession into a career— we’re hiring!

Senior Machine Learning Engineer
Team: Data & Audience Platform (DAP) — ML Engineering

What We Do
Warner Bros. Discovery (WBD) is home to the world’s most iconic entertainment, news, and sports brands — HBO Max, CNN, Discovery+, DC, Warner Bros.,
Bleacher Report, Food Network, and many more. Within the Data & Audience
Platform (DAP) organization, our Machine Learning Engineering team builds the
foundational AI/ML intelligence that powers identity, audience, advertising, and
personalization across every WBD brand. We turn first-party signals from
hundreds of millions of viewers into production ML systems that expand
addressable audiences, sharpen targeting and measurement, forecast demand,
and personalize content discovery — directly driving advertising yield, marketing
efficiency, engagement, and retention.
At WBD, Machine Learning Engineering does rigorous data science and own the
engineering that brings models to life: production ML data pipelines, model
training and optimization, and the ML infrastructure — feature stores, training
and serving pipelines, and MLOps — that makes our work reliable, repeatable,
and scalable. We build primarily on Databricks, with strong working knowledge
of Snowflake and AWS, and we are an early, enthusiastic adopter of agentic AI
development workflows.
 

About the Role:
This is a senior, high-ownership US-based role that sits between our Senior MLE
and Staff MLE levels. You will own the design and delivery of production ML
systems end to end and take on cross-cutting technical leadership: setting
patterns, driving key architectural decisions on flagship workstreams, and raising the bar for the broader ML organization — including close partnership with our Hyderabad ML team. As a US-based senior engineer, you will also serve as a
technical anchor and time-zone bridge across the global team: framing
ambiguous problems, unblocking others, and translating business priorities from US-based Product, Marketing, and Ad Sales stakeholders into an executable ML roadmap.

This role is ideal for engineers with roughly 5–8 years of experience (3+ with a
PhD) who operate with strong autonomy, lead by influence, and can move fluidly
from hands-on modeling and pipeline engineering to architecture and
mentorship. You will do meaningful individual technical work while beginning to
exercise Staff-level scope across initiatives.

What You’ll Do:
ML System Design & Technical Leadership
Lead end-to-end development of production ML systems: data sourcing,
feature engineering, model training, evaluation, deployment, and
monitoring.
Own one or more flagship ML products — e.g., probabilistic identity
resolution (matching unauthenticated device IDs and 1P cookies to
households/persons with calibrated confidence), single-title affinity (two-
tower retrieval), lookalike modeling, or forecasting — and drive their
technical direction.
Make and document key architectural decisions across a workstream
(feature-store design, training/serving patterns, evaluation frameworks);
provide deep trade-off analysis on scalability, latency, reliability, and c