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

Role overview

Staff Machine Learning Engineer - (Growth & ML Team), Hyderabad at Warner Bros. Discovery

Championing Inclusion at WBD.

Responsibilities

• You will set the technical direction for the team’s most complex and strategically important ML systems, serve as the technical authority across multiple concurrent workstreams, and act as a force multiplier for the entire team.
• Lead architectural decisions for the team’s 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 each is the right tool.
• Drive standardization of ML practices across Hyderabad and align with global WBD ML engineering standards.
• Lead the evolution of Audience Intelligence: ML Promo Optimizer (layered retrieval + propensity + closed-loop measurement, toward mixture-of-experts), STAT v2 (two-tower single-title affinity with semantic content embeddings), lookalike modeling (LAL 2.0+) inside Snowflake DCR, and content segmentation.
• Drive the roadmap for bringing ML personalization signals (genre/content affinity, engagement trends) into batch and, over time, real-time activation paths.

Employment details

• Location: Hyderabad [Telangāna] [India]

Additional role details

• 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.
• This role requires 8+ years of experience, exceptional depth across the ML stack, and the ability to influence technical decisions across organizational boundaries.
• Define and own the technical architecture for the team’s core systems: the probabilistic identity spine, audience intelligence platform, content-affinity and genre-preference models, and ML-based forecasting.
• Evaluate and recommend new technologies and approaches (e.g., DCR-native modeling, graph ML, agentic ML orchestration, LLM-augmented pipelines) with clear build/buy/partner assessments.
• Architect and lead delivery of the probabilistic identity resolution system — resolving unauthenticated device IDs and 1P cookies to households/persons with calibrated confidence at scale across all WBD brands — using entity resolution, embeddings/representation learning, calibration, candidate blocking, and champion/challenger promotion, with a roadmap toward person-level graphs (incl.
• GNNs) and real-time resolution.
• Own the ML architecture for forecasting (audience growth, demand, yield/pricing) and ensure models are production-grade, monitored, and continuously improved.
• MLOps Platform Leadership.
• Define the team’s MLOps target architecture: feature contracts, model-registry governance, automated retraining, drift detection, and A/B experimentation infrastructure.
• Establish engineering standards for the full ML lifecycle: data contracts → feature engineering → training → evaluation → deployment → monitoring → deprecation.
• Champion a lightweight, adoptable, well-documented architecture (Databricks Asset Bundles, GitHub Actions CI/CD, MLflow throughout, feature tables, inference/monitoring tables), with leakage prevention, reproducibility, and FinOps controls baked in.
• Serve as the technical anchor for Hyderabad; provide architectural guidance and deep mentorship to Senior and MLE 2 engineers.
• Partner with the Senior ML Engineering Manager on technical roadmap prioritization, 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.
• Produce high-quality technical design documents, architecture reviews, and post-mortems that elevate the team’s engineering culture. 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, Glue) and Snowflake expertise (DCR patterns, Snowpark, Cortex, SQL optimization at scale).
• Proven experience architecting production ML systems serving millions of users, and a track record of technical leadership (setting standards, driving architecture, influencing across teams).
• Expert proficiency with ML frameworks (PyTorch, TensorFlow, XGBoost/LightGBM, scikit-learn) and 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.
• Streaming / media / ad-tech ML: identity resolution, audience modeling, recommendation/ranking, content understanding.
• Deep knowledge of probabilistic identity resolution (entity resolution, graph-based household matching, confidence calibration) and Data Clean Room ML (Snowflake DCR, AWS Clean Rooms).
• Hands-on experience with agentic AI frameworks at production scale (LangGraph, AutoGen, MCP, CrewAI), Databricks Genie Space curation, and Snowflake Cortex Search / Fine-Tuning for enterprise RAG.
• Experience with real-time feature serving and low-latency inference, and with mixture-of-experts or graph neural networks.
• Primary platform: Databricks (Lakehouse, PySpark, Delta, Workflows/DLT, MLflow, Feature Store, Unity Catalog, Asset Bundles, Genie).
• Cloud: AWS (SageMaker, S3, Lambda, Glue).
• Warehouse: Snowflake (DCR, Snowpark, Cortex).
• Agentic AI: Cursor, GitHub Copilot, Amazon Q, Databricks Genie, Snowflake Cortex, MCP.
• Languages: Python (primary), SQL, Scala (as needed).