Machine Learning Engineer II (Data & Audience Platform Team), Hyderabad

What You ll Do

ML Development & Modeling

- Build and maintain end-to-end ML pipelines for training, evaluation, and batch inference across use cases such as identity resolution, audience segmentation, and content affinity modeling.
- Implement and experiment with supervised, unsupervised, and ranking models in Python (scikit-learn, XGBoost/LightGBM, PyTorch).
- Engineer features from first-party viewership, engagement, subscription, and behavioral signals, guarding against data leakage, collinearity, and training/serving skew.
- Run structured offline experiments; evaluate with the right metrics (precision/recall, F1, AUC-ROC, calibration, lift) and document findings in MLflow.

ML Infrastructure & Engineering

- Develop and maintain data and feature pipelines on Databricks (PySpark, Delta, Workflows) that feed the feature store and model-training workflows, with attention to idempotency and reproducibility.
- Write clean, tested, production-quality Python following engineering best practices (unit tests, code reviews, CI/CD).
- Use MLflow for experiment tracking, model registration, and versioning under the guidance of senior engineers.
- Support deployment and monitoring of batch inference jobs integrated with downstream activation platforms (e.g., Mosaic, FreeWheel, GAM) and data in Snowflake.

Agentic AI & Modern Tooling

- Use AI-assisted development tools (Cursor, GitHub Copilot, Amazon Q) to accelerate coding, debugging, and documentation under guidance.




- Leverage Databricks Genie for natural-language exploration of governed Unity Catalog datasets querying ML feature tables, model outputs, and audience segments.
- Use Snowflake Cortex (Copilot / Cortex Analyst) to accelerate data analysis and SQL authoring against audience and identity schemas.
- Learn and apply prompt-engineering patterns for LLM-assisted data exploration and feature generation, and participate in evaluating MCP (Model Context Protocol) tooling as the team expands agentic workflows.

Collaboration & Growth

- Partner with Senior and Staff MLEs to understand system-design decisions and contribute meaningfully to technical discussions.
- Work cross-functionally with Data Engineering, Feature Engineering, and Analytics to ensure data quality and pipeline reliability.
- Document models, pipelines, and experiments clearly for team knowledge sharing.

What You ll Bring

Required

- 2-4 years of industry experience in machine learning, data science, or ML engineering (or 1-2 years with a relevant M.S.).
- Solid Python proficiency; experience with pandas, NumPy, scikit-learn, and at least one deep-learning framework (PyTorch or TensorFlow).
- Hands-on experience with Spark/PySpark or equivalent large-scale data processing.

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.

📌 Machine Learning Engineer II ( Data & Audience Platform Team) (Hyderabad)
🏢 Warner Bros. Discovery
📍 Hyderabad