Machine Learning Engineer II ( Growth & ML), Hyderabad

Role overview

Machine Learning Engineer II ( Growth & ML), Hyderabad at Warner Bros. Discovery

Some Important Details.

As a Machine Learning Engineer II, you will work alongside Senior and Staff engineers to build and maintain production ML pipelines, contribute to model development, and grow into a well-rounded ML engineer who bridges data science and software engineering.

Responsibilities

• 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.
• 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).
• Support deployment and monitoring of batch inference jobs integrated with downstream activation platforms (e.g., Mosaic, FreeWheel, GAM) and data in Snowflake.
• Required.
• Preferred.

Required qualifications

• Strong Python proficiency; experience with pandas, NumPy, scikit-learn, and at least one deep-learning framework (PyTorch or TensorFlow).

Employment details

• Location: Hyderabad [Telangāna] [India]

Application details

• 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.

Additional role details

• This is a hands-on role for someone with roughly 2–4 years of experience who is eager to work on high-impact ML systems at scale — from probabilistic identity graphs to single-title affinity models — and who wants to develop deep expertise across the full ML lifecycle.
• ML Development & 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.
• Use MLflow for experiment tracking, model registration, and versioning under the guidance of senior engineers.
• 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.
• 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. 2–4 years of industry experience in machine learning, data science, or ML engineering (or 1–2 years with a relevant M.S.).
• Hands-on experience with Spark/PySpark or equivalent large-scale data processing.
• Proficiency in SQL and familiarity with cloud data warehouses/lakehouses (Snowflake or Databricks).
• Experience with experiment-tracking tools (MLflow, Weights & Biases, or similar).
• Solid grasp of core ML concepts: classification, regression, ranking, embeddings, and model evaluation; plus strong CS fundamentals (data structures, algorithms, clean code).
• Bachelor’s degree in Computer Science, Statistics, Engineering, or a related quantitative field (or equivalent practical experience).
• Ability to use AI tools to independently improve productivity across the ML lifecycle, and clear written and verbal communication.
• Experience with Databricks (notebooks, Delta Lake, Workflows/DLT, Unity Catalog, MLflow).
• Exposure to recommendation systems, audience segmentation, identity resolution, or forecasting.
• Familiarity with AWS services (SageMaker, S3, Lambda) and/or feature stores (Databricks Feature Store, Feast).
• Exposure to agentic AI tools: Cursor, GitHub Copilot, Amazon Q, Databricks Genie, Snowflake Cortex, or MCP.
• 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.