Staff Machine Learning Engineer - (Growth & ML Team), Hyderabad

About the Role The Staff Machine Learning Engineer is the senior-most individual contributor on the Hyderabad ML Engineering team within the Data & Audience Platform organisation. The role sets technical direction for the team’s most complex and strategically important ML systems, serves as technical authority across multiple concurrent workstreams, and acts as a force multiplier for the team. It owns the architecture of WBD’s ML capabilities in Hyderabad, spanning identity intelligence, audience intelligence, content affinity, and forecasting. The position partners closely with the Senior ML Engineering Manager and Director and influences technical decisions across organisational boundaries. Key Responsibilities Define and own the technical architecture for core systems: the probabilistic identity spine, audience intelligence platform, content-affinity and genre-preference models, and ML-based forecasting. Lead architectural decisions for the MLOps framework — feature-store design, training-pipeline standards, model-serving patterns, and monitoring infrastructure — on a Databricks-first architecture, integrating Snowflake and AWS SageMaker where appropriate. Evaluate and recommend new technologies and approaches, such as DCR-native modelling, graph ML, agentic ML orchestration, and LLM-augmented pipelines, with clear build/buy/partner assessments. Drive standardisation of ML practices across Hyderabad and align with global WBD ML engineering standards. Architect and lead delivery of the probabilistic identity resolution system, resolving unauthenticated device IDs and first-party cookies to households or persons with calibrated confidence at scale, using entity resolution, embeddings/representation learning, calibration, candidate blocking And champion/challenger promotion, with a roadmap toward person-level graphs including GNNs and real-time resolution. Lead the evolution of Audience Intelligence, including ML Promo Optimizer, STAT v2, lookalike modelling inside Snowflake DCR, and content segmentation. Own the ML architecture for forecasting audience growth, demand, and yield/pricing, ensuring models are production-grade, monitored, and continuously improved. Drive the roadmap for bringing ML personalisation signals such as genre/content affinity and engagement trends into batch and, over time, real-time activation paths. Define the team’s MLOps target architecture, including feature contracts, model-registry governance, automated retraining, drift detection, and A/B experimentation infrastructure. Establish engineering standards for the full ML lifecycle from data contracts and feature engineering through training, evaluation, deployment, monitoring, and deprecation, and champion a lightweight, adoptable, well-documented architecture using Databricks Asset Bundles, GitHub Actions CI/CD MLflow, feature tables, and inference/monitoring tables, with leakage prevention, reproducibility, and FinOps controls. Serve as the technical anchor for Hyderabad, providing architectural guidance and deep mentorship to Senior and MLE 2 engineers, and partner with the Senior ML Engineering Manager on technical roadmap prioritisation, headcount planning, and team capability development. Represent the Hyderabad team in cross-functional technical forums with US-based ML, Data Engineering, Product, and Ad Sales stakeholders, and produce high-quality technical design documents, architecture reviews, and post-mortems that elevate the team’s engineering culture. Required Experience & Qualifications 8+ years of industry experience in ML engineering (6+ with a Ph.D.), with demonstrated Staff-level scope and impact. Mastery of the full ML stack: data engineering, feature engineering, model development, MLOps, and production monitoring. Deep Databricks expertise: Delta Lake, Unity Catalog, Workflows/DLT, MLflow, Feature Store, Asset Bundles, and Genie Space configuration. Strong AWS proficiency: SageMaker training/pipelines/model registry, S3, Lambda, and Glue. Snowflake expertise: DCR patterns, Snowpark, Cortex, and SQL optimisation at scale. Proven experience architecting production ML systems serving millions of users. Track record of technical leadership, including setting standards, driving architecture, and influencing across teams. Expert proficiency with ML frameworks: PyTorch, TensorFlow, XGBoost/LightGBM, and scikit-learn. Deep understanding of statistics and ML fundamentals. Master’s or Ph.D. in Computer Science, Statistics, Machine Learning, or a related field, or equivalent industry experience. Excellent communication, with the ability to advocate technical solutions to engineering, science, product, and executive audiences. Ability to influence technical decisions across organisational boundaries. Preferred Skills Streaming, media, or ad-tech ML experience: identity resolution, audience modelling, recommendation/ranking, and content understanding. Deep knowledge of probabilistic identity resolution: entity resolution, graph-based household matching, and confidence calibration. Data Clean Room ML experience: Snowflake DCR and AWS Clean Rooms. Hands-on experience with agentic AI frameworks at production scale: LangGraph, AutoGen, MCP, and CrewAI. Databricks Genie Space curation. Snowflake Cortex Search / Fine-Tuning for enterprise RAG. Experience with real-time feature serving and low-latency inference. Experience with mixture-of-experts or graph neural networks. Published research or conference presentations in relevant ML domains. Benefits & Perks A Great Place to work. Fast track growth opportunities. Thoughtfully curated benefits. Tools to explore and grow. 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. #J-18808-Ljbffr Global Sports Jobs Limited