Senior Machine Learning Engineer, Hyderabad

Build ML models for identity resolution, audience intelligence, content affinity modeling, genre-preference modeling and time-series forecasting across global markets. Integrate with personalization systems to consume in-app user signals. Design feature pipelines that fuse real-time streaming signals with batch-computed features for online scoring. Partner with Product, Engineering, and Data Science to translate business problems into well-scoped ML solutions. Evaluate new technologies and approaches, including DCR-native modeling, graph ML, agentic ML orchestration, and LLM-augmented pipelines, with clear build/buy/partner recommendations. Lead the evolution of Audience Intelligence, including ML Promo Optimizer, STAT v2, lookalike modeling inside Snowflake DCR, and content segmentation. Own ML architecture for forecasting use cases such as audience growth, demand, yield, and pricing, ensuring models are monitored and continuously improved. Bring ML personalization signals, such as genre/content affinity and engagement trends, into batch and future real-time activation paths. Experimentation, Quality & ObservabilityDesign offline and online evaluation approaches with clear baselines, success metrics, experiment design, and promotion criteria. Improve feature and label quality, leakage prevention, bias checks, calibration, explainability where applicable, and impact measurement. Identify technical risks early, document tradeoffs, and drive mitigation plans with engineering, data, product, and business partners. Turn incidents and postmortems into reusable standards, automated checks, and platform improvements. Cross-functional Partnership & MentorshipMentor MLE 2 engineers through architecture reviews, design discussions, implementation guidance, and hands-on problem solving. Partner with the ML leads on technical roadmap shaping, execution planning, capability development, and technical input into hiring profiles. Represent the Hyderabad team in technical forums with ML, Data Engineering, Product, Ad Sales, and US-based stakeholders. Create clear design docs, architecture reviews, readiness reviews, and postmortems that strengthen engineering culture. What You’ll Bring:5 - 8 years of ML engineering experience, or 2+ years with a Ph.D., with demonstrated Senior-level scope, technical ownership, and cross-team impact. You have worked on optimization problems like ranking, personalization, or multi-objective decisioning in environments with interacting metrics. You have hands on experience with Databricks, Spark and Sagemaker. Experience architecting production ML systems for large user populations, with a track record of setting standards and influencing across teams. Excellent communication skills, with the ability to explain technical solutions to engineering, science, product, and executive audiences. PreferredStreaming / Identity / Fraud / Ad-tech ML: identity resolution, audience modeling, recommendation/ranking, content understanding. Hands-on experience with agentic AI frameworks at production scaleExperience with real-time feature serving and low-latency inference, and with mixture-of-experts or graph neural networks. Our Technology Stack Primary platform: AWS , Databricks. 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. 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). Languages: Python (primary), SQL, Scala (as needed). Championing Inclusion at WBD.