Senior Machine Learning Engineer

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., 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 cost.

Design scalable feature and inference pipelines on Databricks (PySpark,

Delta, Workflows/DLT, Unity Catalog) integrated with Snowflake and

activation systems (Mosaic, FreeWheel, GAM), with documented feature

contracts, backfill paths, and freshness SLAs.

Establish and evangelize patterns that other engineers adopt; anticipate

risks and failure modes before they surface.

Modeling & Experimentation

Develop and optimize models across the ML spectrum: gradient boosting

(XGBoost/LightGBM), embedding/two-tower retrieval, neural ranking,

probability calibration (e.g., isotonic regression), and probabilistic/graph-

based matching.

Design rigorous offline and online experiments; define evaluation

frameworks (precision/recall, AUC-ROC, NDCG, decile lift, calibration

curves) appropriate to each use case.

Apply causal-inference techniques (propensity scoring,

uplift/incrementality modeling) to measure true lift of audience targeting

on engagement and retention KPIs.

Contribute to lookalike modeling (LAL 2.0+) using 1,000+ first- and third-

party features, including privacy-safe builds inside Data Clean Rooms

(Snowflake DCR).

MLOps & Infrastructure

Champion MLOps best practices: model versioning, champion/challenger

promotion, automated retraining triggers, drift detection, and production

monitoring with MLflow on Databricks.

Build and maintain robust, reproducible, auditable ML pipelines on

Databricks (and AWS SageMaker where appropriate, e.g., the identity-

resolution track); enforce leakage prevention and training/serving

consistency.

Shape the team's feature-store strategy - feature contracts, backfills, and

freshness SLAs - and implement data-quality checks, model-health

dashboards, and alerting thresholds.

Embed FinOps cost discipline (compute caps, auto-termination, job tagging)

into pipeline design.

Agentic AI & Modern Development

Actively use and advocate for AI-assisted development: Cursor, GitHub

Copilot, and Amazon Q for code generation, review, and documentation.

Leverage Databricks Genie as a governed natural-language analytics layer

• configuring Genie Spaces over ML feature tables and audience datasets

to enable self-service exploration for cross-functional stakeholders.

Use Snowflake Cortex (Copilot, Cortex Analyst, Cortex Search) to

accelerate SQL authoring, data discovery, and RAG-based internal tooling

over Snowflake-resident identity and audience data.

Design and prototype agentic ML workflows (MCP-compatible tooling,

LangChain/LangGraph) to automate repetitive tasks such as data

validation, feature selection, and hyperparameter search; evaluate LLM-

based approaches for metadata enrichment and content understanding.

Mentorship & Cross-functional Collaboration

Mentor Senior and MLE 2 engineers - including members of the

Hyderabad team - through code reviews, design discussions, and pairing;

contribute to and help set team technical standards.

Serve as a US-based point of contact and time-zone bridge for the global ML

team; help align priorities and unblock the India team across time zones.

Partner with US-based Product, Marketing, and Ad Sales stakeholders to

translate business requirements into ML problem formulations, and with

Data Engineering on data contracts and pipeline SLAs.

Communicate model performance, trade-offs, and business impact clearly

to technical and non-technical stakeholders.

Flagship Projects You'll Work On

Identity Intelligence - foundational, privacy-safe identity across all WBD

brands: probabilistic ID resolution that resolves unauthenticated signals to

households/persons with calibrated confidence (entity resolution with

gradient boosting and embeddings, representation learning, isotonic

calibration, candidate blocking, champion/challenger pipelines), expanding

addressable audiences beyond deterministic matching.

Audience Intelligence - advertising and marketing use cases: lookalike

and predictive audiences (LAL across 1,000+ features), ML-driven smart

audiences, layered retrieval + propensity, and incrementality/closed-loop

optimization, with privacy-safe activation including data clean rooms.

ML-based Forecasting - audience growth, demand, and advertising

yield/pricing forecasting that powers ad sales and marketing decisions.

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., 5-8 years of industry experience in ML engineering or applied data science

(3+ years with a Ph.D.), including a track record of leading projects to

production.

Deep Python expertise and strong software engineering practices;

production experience building and deploying ML at scale (millions+ of

users/records).

Strong proficiency in Databricks (PySpark, Delta Lake, Workflows/DLT,

MLflow, Unity Catalog) and solid SQL/Snowflake experience for feature

sourcing and model-output delivery.

Experience with AWS ML services (SageMaker, S3, Lambda).

Strong understanding of ML model evaluation, A/B testing, and

statistical/causal inference; depth in one or more of recommendations &

ranking, identity resolution, embeddings/retrieval, forecasting, or

optimization.

Demonstrated technical leadership: driving architectural decisions, setting

patterns/standards, and mentoring other engineers - including leading by

influence across teams and time zones.

Bachelor's or Master's degree in Computer Science, Statistics, Engineering,

or a related quantitative field (or equivalent experience).

Excellent written and verbal communication, with the ability to advocate

technical solutions to engineers, scientists, and product stakeholders.

Preferred:

Recommendation systems, personalization, identity resolution, or audience

modeling in a media / streaming / ad-tech context.

Experience with two-tower / retrieval architectures, probabilistic identity

resolution (graph-based matching, entity resolution, confidence

calibration), and Data Clean Room ML (Snowflake DCR, AWS Clean Rooms).

Experience architecting or standardizing components of an ML platform

used by multiple engineers or teams.

Hands-on experience with agentic AI frameworks (LangChain, LangGraph,

AutoGen, MCP), Databricks Genie Space configuration, and Snowflake

Cortex.

Experience with feature stores (Databricks Feature Store, Tecton, Feast)

and contributions to open source or ML publications.

Experience partnering with or mentoring globally distributed teams.

Our Technology Stack

Primary platform: Databricks (Lakehouse, PySpark, Delta, Workflows/DLT,

MLflow, Feature Store, Unity Catalog, Asset Bundles, Genie). Cloud: AWS

(SageMaker, S3, Lambda). Warehouse: Snowflake (incl. DCR, Snowpark, Cortex).

Activation: Mosaic, FreeWheel, Google Ad Manager. Agentic AI: Cursor, GitHub

Copilot, Amazon Q, Databricks Genie, Snowflake Cortex, MCP. Languages: Python

(primary), SQL, Scala (as needed).

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., In compliance with local law, we are disclosing the compensation, or a range thereof, for roles in locations where legally required. Actual salaries will vary based on several factors, including but not limited to external market data, internal equity, location, skill set, experience, and/or performance. Base pay is just one component of Warner Bros. Discovery's total compensation package for employees. Pay Range: $159,180.00 - $295,620.00 salary per year. Other rewards may include annual bonuses, short- and long-term incentives, and program-specific awards. In addition, Warner Bros. Discovery provides a variety of benefits to employees, including health insurance coverage, an employee wellness program, life and disability insurance, a retirement savings plan, paid holidays and sick time and vacation.

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!