Senior Machine Learning Engineer(Growth & ML), Hyderabad

Global Sports Jobs Limited; Phoenix (AZ); On-site; USD 140,000 - 210,000; Full time

Job summary

Warner Bros. Discovery seeks a Senior Machine Learning Engineer to own production ML systems across the Data & Audience Platform, driving problem framing through deployment and monitoring.

You will lead ML products like probabilistic identity resolution and lookalike modelling, while mentoring MLEs and collaborating with product stakeholders. You will design scalable pipelines on Databricks and Snowflake, build batch and streaming inference, and apply advanced evaluation and causal techniques to

Qualifications

• 5-8 years of ML engineering or applied data science experience (3+ years with PhD).
• Deep Python expertise and production ML at scale.
• Strong Databricks, 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 inference; knowledge in recommendations, identity resolution, embeddings/retrieval, forecasting or optimisation.
• Demonstrated ability to lead technical decisions and mentor engineers.
• Bachelor's or Master's degree in CS/Statistics/Engineering or related field (or equivalent).
• Excellent written and verbal communication.

Responsibilities

• Lead end-to-end development and delivery of production ML systems, covering data sourcing, feature engineering, model training, evaluation, deployment and monitoring.
• Own ML products including probabilistic identity resolution, single-title affinity and lookalike modelling.
• Design scalable feature pipelines on Databricks and the WBD feature store with documented feature contracts and SLAs.
• Architect batch and near-real-time inference pipelines integrated with Snowflake and activation systems.
• Develop and optimise models across the ML spectrum including gradient boosting, embeddings, and probabilistic matching.
• Design offline and online experiments and evaluation frameworks (precision/recall, AUC-ROC, NDCG, calibration).
• Apply causal-inference techniques to measure lift of audience targeting on KPIs.
• Champion MLOps practices, including model versioning and monitoring with MLflow.
• Build robust, auditable ML pipelines with cost discipline and governance.
• Mentor MLEs and contribute to technical standards.

Skills

Python

ML engineering

Databricks

SQL

SageMaker

A/B testing

Model evaluation

Mentoring

Education

Bachelor's or Master's in CS/Statistics/Engineering

PhD in a related field

Tools

Delta Lake

MLflow

Unity Catalog

Snowflake

SageMaker

Job description

About the Role

Within Warner Bros. Discovery's Data & Audience Platform (DAP) organisation, the Hyderabad Machine Learning Engineering team builds the AI/ML intelligence that powers identity, audience, advertising and personalisation across WBD brands. The Senior Machine Learning Engineer will own the design and delivery of production ML systems that directly impact audience targeting, advertising revenue, subscriber engagement and retention across WBD's global portfolio. The role will lead technical execution on workstreams such as probabilistic identity resolution, lookalike modelling, single-title affinity and forecasting, while mentoring MLE 2s and collaborating with Staff MLEs and product stakeholders. It is a high-ownership role for engineers with roughly 5-8 years of experience who can independently drive projects from problem framing through production deployment and monitoring.

Key Responsibilities

• Lead end-to-end development and delivery of production ML systems, covering data sourcing, feature engineering, model training, evaluation, deployment and monitoring.
• Own key ML products including probabilistic identity resolution, single-title affinity (e.g., STAT two-tower retrieval), audience/propensity models and lookalike modelling (LAL 2.0+) using 1,000+ first- and third-party features, including privacy-safe builds inside Data Clean Rooms.
• Design scalable feature pipelines on Databricks (PySpark, Delta, Workflows/DLT, Unity Catalog) and the WBD feature store with documented feature contracts, backfill paths and freshness SLAs
• Architect batch and near-real-time inference pipelines integrated with Snowflake and activation systems (Mosaic, FreeWheel, GAM).
• Develop and optimise models across the ML spectrum, including 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 and 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 modelling) to measure true lift of audience targeting on engagement and retention KPIs.
• Champion MLOps best practices, including 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), enforcing leakage prevention and training/serving consistency, and embed FinOps cost discipline (compute caps, auto-termination, job tagging) into pipeline design.
• Contribute to the team's feature-store strategy and implement data-quality checks, model-health dashboards and alerting thresholds.
• Mentor MLE 2s through code reviews, design discussions and pairing
• Contribute to team technical standards
• Partner with Product, Marketing and Ad Sales on ML problem formulation and with Data Engineering on data contracts and pipeline SLAs
• Communicate model performance, trade-offs and business impact clearly.

Required Experience & Qualifications

• 5-8 years of industry experience in ML engineering or applied data science (3+ years with a Ph.D.).
• Deep Python expertise and production-quality 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 inference; knowledge in one or more of recommendations & ranking, identity resolution, embeddings/retrieval, causal/interpretable ML, forecasting, bandits or optimisation.
• Demonstrated ability to lead technical decisions and mentor engineers.
• 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 Skills

• Recommendation systems, personalisation, identity resolution, or audience modelling in a media / streaming / ad-tech context.
• Familiarity with Data Clean Room environments (Snowflake DCR, AWS Clean Rooms) and consent-aware activation.
• Experience with two-tower / retrieval architectures, probabilistic identity resolution (graph-based matching, entity-resolution), and probability calibration.
• Hands-on experience with agentic AI frameworks (LangChain, LangGraph, AutoGen, MCP), Databricks Genie Space configuration, and Snowflake
• Experience with feature stores (Databricks Feature Store, Tecton, Feast) and contributions to open source or ML publications.

About the Company

The company combines Warner Media's premium entertainment, sports and news assets with Discovery's leading non-fiction and international entertainment and sports businesses, and is home to brands including HBO Max, CNN, Discovery+, DC, Warner Bros., Bleacher Report and Food Network.