Description

Key Skills: Machine Learning, Python, AWS, Databricks, Snowflake, PyTorch, TensorFlow, Spark, MLOps, Data Science

Good to Have Skills: Agentic AI frameworks, XGBoost, LightGBM, scikit-learn, Sagemaker, real-time feature serving, low-latency inference, mixture-of-experts, graph neural networks, Scala, SQL, identity resolution, audience modeling, recommendation systems, ranking algorithms, content understanding, fraud detection, ad-tech ML, streaming platforms experience, published research in ML domains.

Roles & Responsibilities:

• Design and operate low latency online serving systems for fraud scoring, message decisioning and real-time personalization.
• 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 for enhanced user experience.
• Design feature pipelines that fuse real-time streaming signals with batch-computed features for online scoring.
• Partner with Product, Engineering, and Data Science teams 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.
• Act as a senior technical anchor for the team, improving design quality, code quality, reviews, and execution.
• Architect probabilistic identity resolution systems that connect unauthenticated device IDs and first-party cookies to households with calibrated confidence.
• Lead the evolution of Audience Intelligence, including ML Promo Optimizer, STAT v2, lookalike modeling and content segmentation.
• Own ML architecture for forecasting use cases such as audience growth, demand, yield, and pricing optimization.
• Design offline and online evaluation approaches with clear baselines, success metrics, experiment design, and promotion criteria.
• Mentor Senior and MLE 2 engineers through architecture reviews, design discussions, implementation guidance, and hands-on problem solving.
• Partner with ML Engineering Manager 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.

Experience Required: 9-13 years of ML engineering experience, or 6+ years with a Ph.D., with demonstrated Staff-level scope, technical ownership, and cross-team impact. Experience architecting production ML systems for large user populations with track record of setting standards and influencing across teams. Hands-on experience with optimization problems like ranking, personalization, or multi-objective decisioning in environments with interacting metrics.

Education: Master's or Ph.D. in Computer Science, Statistics, Machine Learning, or a related field (or equivalent industry experience)

Key Skills

Education

Any Graduate