Machine Learning Engineer II ( Growth & ML), Hyderabad

Storytelling is, and will always be, the heart of what we do at Skydance.

Our vision is to build a thriving entertainment ecosystem where stories and worlds are imagined, brought to life and experienced. We create for every age and audience, anchored by the strength of our content and experiences and the depth of our connection with fans. We are creator-first, audience-focused, tech-forward, globally scaled.

Here, you’ll work with talented teams that move fast, think boldly and collaborate with purpose to deliver results. We aim high, champion the best ideas and win together, earning the trust of our employees, creators, partners and audiences every day.

If you’re energized by the opportunity to help build the next-generation global media and entertainment company powered by creativity and technology, we’d be thrilled to welcome you aboard. LET’S GO!

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Machine Learning Engineer II ( Growth & ML Team), Hyderabad

Meet our Team

Our Data & Audience Platform organization builds advanced AI/ML solutions that power identity, audience intelligence, advertising, and personalization at scale. Our Machine Learning Engineering team in Hyderabad develops foundational AI/ML capabilities that transform first-party data signals into production-ready machine learning systems. These solutions help expand addressable audiences, improve targeting and measurement, forecast demand, and personalize content discovery — driving advertising yield, marketing efficiency, user engagement, and retention.

Our Machine Learning Engineers combine strong data science expertise with hands-on engineering capabilities to take models from development to production. This includes building production-grade ML data pipelines, model training and optimization, and scalable ML infrastructure such as feature stores, training and serving pipelines, and MLOps frameworks.

We primarily leverage Databricks, along with Snowflake and AWS, and actively embrace emerging technologies, including agentic AI development workflows, to build reliable, scalable, and intelligent machine learning solutions

About the Role

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. 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.

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
3-5 years of industry experience in machine learning, data science, or ML engineering (or 2–3 years with a relevant M.S.).

Strong 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.

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.

Preferred

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.

What We Offer:

• A Great Place to work
• Equal opportunity employer
• Fast track growth opportunities

About Skydance

Skydance is a next-generation global media and entertainment company, comprised of three business segments: Studios, Direct-to-Consumer, and TV Media. Skydance's portfolio unites legendary brands, including Paramount, Warner Bros., HBO and HBO Max, Paramount+, Pluto TV, CBS, CNN, CBS Sports, TNT Sports, Nickelodeon, Cartoon Network, MTV, Food Network, BET, HGTV, and Comedy Central.

Some Important Details

We embrace the opportunity to build a workforce that reflects a wide array of perspectives, backgrounds and experiences. Being an equal opportunity employer means that we take seriously our responsibility to consider qualified candidates on the basis of merit. In line with the laws of the country or region in which the role is based, this includes consideration without regard to race, color, ethnicity, national origin, citizenship status, religion or belief, sex, gender, gender identity or expression, sexual orientation, age, marital status, pregnancy, parenthood, physical or mental disability, genetic information, military or protected veteran status, union status or any other category protected by applicable law.

If you’re a qualified candidate with a disability and you require adjustments or accommodations during the job application and/or recruitment process, please visit our accessibility page for instructions to submit your request.