Staff Machine Learning Engineer (Data & Audience Platform Team)

Staff Machine Learning Engineer (Data & Audience Platform), Hyderabad About the Role The Staff MLE is the senior-most individual contributor on the Hyderabad ML Engineering team. 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. You will own the architecture of WBD's ML capabilities in Hyderabad spanning identity intelligence, audience intelligence, content affinity, and forecasting and be a key technical partner to the Senior ML Engineering Manager and Director. This role requires 8+ years of experience, exceptional depth across the ML stack, and the ability to influence technical decisions across organizational boundaries. What You ll Do Technical Vision & Architecture 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. 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. 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. Drive standardization of ML practices across Hyderabad and align with global WBD ML engineering standards. Flagship ML System Ownership 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. 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. Own the ML architecture for forecasting (audience growth, demand, yield/pricing) and ensure models are production-grade, monitored, and continuously improved. Drive the roadmap for bringing ML personalization signals (genre/content affinity, engagement trends) into batch and, over time, real-time activation paths. 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. Agentic AI & Emerging Capabilities Lead the team s adoption of agentic AI development: define standards for using Cursor, GitHub Copilot, and Amazon Q in production ML workflows, and for MCP-based tooling. Architect the team s use of Databricks Genie at scale Genie Space governance, Unity Catalog semantic-layer standards, and knowledge-store curation so ML outputs (audience scores, identity confidence, affinity signals) are self-serviceable by Marketing, Ad Sales, and Product without engineering intervention. Own the team s Snowflake Cortex strategy Cortex Analyst / Copilot embedded into internal tooling, Cortex Search for RAG-based internal knowledge, and (where valuable) Cortex Fine-Tuning on WBD audience/identity vocabulary. Architect agentic ML workflows for high-value automation (feature-selection and hyperparameter agents, data-quality monitoring agents, model-card generation) and evaluate emerging paradigms (DCR-native training, real-time feature serving) for the production roadmap. Organizational Influence & Mentorship 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. Disclaimer : This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.