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About the Role

This senior, high-ownership US-based role 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, and raising the bar for the broader ML organization. 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 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.

Responsibilities

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 and drive their technical direction.
Make and document key architectural decisions across a workstream; provide deep trade-off analysis on scalability, latency, reliability, and cost.
Design scalable feature and inference pipelines on Databricks integrated with Snowflake and activation systems, 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.
Develop and optimize models across the ML spectrum.
Design rigorous offline and online experiments; define evaluation frameworks appropriate to each use case.
Apply causal-inference techniques to measure true lift of audience targeting on engagement and retention KPIs.
Contribute to lookalike modeling using first- and third-party features, including privacy-safe builds inside Data Clean Rooms.
Champion MLOps best practices: model versioning, champion/challenger promotion, automated retraining triggers, drift detection, and production monitoring.
Build and maintain robust, reproducible, auditable ML pipelines on Databricks (and AWS SageMaker where appropriate); 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 into pipeline design.
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.
Use Snowflake Cortex to accelerate SQL authoring, data discovery, and RAG-based internal tooling over Snowflake-resident identity and audience data.
Design and prototype agentic ML workflows to automate repetitive tasks such as data validation, feature selection, and hyperparameter search; evaluate LLM-based approaches for metadata enrichment and content understanding.
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.

Requirements

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.
Recommendation systems, personalization, identity resolution, or audience modeling in a media / streaming / ad-tech context.
Experience with two-tower / retrieval architectures, probabilistic identity resolution, and Data Clean Room ML.
Experience architecting or standardizing components of an ML platform used by multiple engineers or teams.
Hands-on experience with agentic AI frameworks, Databricks Genie Space configuration, and Snowflake Cortex.
Experience with feature stores and contributions to open source or ML publications.
Experience partnering with or mentoring globally distributed teams.

Skills

Python
Databricks
PySpark
Delta Lake
Workflows/DLT
MLflow
Unity Catalog
Snowflake
SQL
AWS SageMaker
AWS S3
AWS Lambda
ML model evaluation
A/B testing
Statistical inference
Causal inference
Recommendations & ranking
Identity resolution
Embeddings/retrieval
Forecasting
Optimization
Agentic AI frameworks
LangChain
LangGraph
AutoGen
MCP
Databricks Genie Space
Snowflake Cortex
Feature stores
Tecton
Feast

Location

US-based

Work Type

Full-time

Experience Level

Senior
5-8 years
3+ years with PhD

Education Level

Bachelor's or Master's degree in Computer Science, Statistics, Engineering, or a related quantitative field (or equivalent experience)

Salary/Compensations

$159,180.00 - $295,620.00 salary per year

Benefits

Thoughtfully curated benefits
Health insurance coverage
Employee wellness program
Life and disability insurance
Retirement savings plan
Paid holidays
Sick time
Vacation

About the Company

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 owns 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.

Equal Opportunity

Warner Bros. Discovery is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.
Warner Bros. Discovery embraces the opportunity to build a workforce that reflects a wide array of perspectives, backgrounds and experiences. Being an equal opportunity employer means that we take seriously our responsibility to consider qualified candidates on the basis of merit, without regard to race, color, religion, national origin, gender, sexual orientation, gender identity or expression, age, mental or physical disability, and genetic information, marital status, citizenship status, military status, protected veteran status or any other category protected by law.
If you’re a qualified candidate with an arrest or conviction record, your application will be considered in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.