Senior Machine Learning Engineer(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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Senior Machine Learning Engineer (Growth & ML), 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 Senior MLE, you will own the design and delivery of production ML systems that directly impact audience targeting, advertising revenue, subscriber engagement, and retention across our global portfolio. You will lead technical execution on key workstreams — including probabilistic identity resolution, lookalike modeling, single-title affinity, and forecasting — while mentoring MLE 2s and collaborating closely with Staff MLEs and product stakeholders. This is a high-ownership role for engineers with roughly 5–8 years of experience who can independently drive a project from problem framing through production deployment and monitoring.

Roles & Responsibilities:

ML System Design & Ownership

• Lead end-to-end development of production ML systems: data sourcing, feature engineering, model training, evaluation, deployment, and monitoring.
• Own key ML products such as probabilistic identity resolution (matching unauthenticated device IDs and 1P cookies to households/persons with calibrated confidence), single-title affinity (e.g., STAT two-tower retrieval), and audience/propensity models.
• Design scalable feature pipelines on Databricks (PySpark, Delta, Workflows/DLT, Unity Catalog) and the feature store, with documented feature contracts, backfill paths, and freshness SLAs.
• Architect batch and near-real-time inference pipelines integrated with Snowflake and activation systems (Mosaic, FreeWheel, GAM).
  

Modeling & Experimentation

• Develop and optimize models across the ML spectrum: gradient boosting (XGBoost/LightGBM), embedding/two-tower retrieval, neural ranking, probability calibration (e.g., isotonic regression), and probabilistic/graph-based matching.
• Design rigorous offline and online experiments; define evaluation frameworks (precision/recall, AUC-ROC, NDCG, decile lift, calibration curves) appropriate to each use case.
• Apply causal-inference techniques (propensity scoring, uplift/incrementality modeling) to measure true lift of audience targeting on engagement and retention KPIs.
• Contribute to lookalike modeling (LAL 2.0+) using 1,000+ first- and third-party features, including privacy-safe builds inside Data Clean Rooms (Snowflake DCR).

MLOps & Infrastructure

• Champion MLOps best practices: model versioning, champion/challenger promotion, automated retraining triggers, drift detection, and production monitoring with MLflow on Databricks.
• Build and maintain robust, reproducible, auditable ML pipelines on Databricks (and AWS SageMaker where appropriate, e.g., the identity-resolution track); enforce leakage prevention and training/serving consistency.
• Contribute to the team’s feature-store strategy — feature contracts, backfills, and freshness SLAs — and implement data-quality checks, model-health dashboards, and alerting thresholds.
• Embed FinOps cost discipline (compute caps, auto-termination, job tagging) into pipeline design.

Mentorship & Cross-functional Collaboration

• Mentor MLE 2s through code reviews, design discussions, and pairing; contribute to team technical standards.
• Partner with Product, Marketing, and Ad Sales to translate business requirements into ML problem formulations, and with Data Engineering on data contracts and pipeline SLAs.
• Communicate model performance, trade-offs, and business impact clearly to technical and non-technical stakeholders.

Flagship Projects You’ll Work On

• Identity Intelligence — foundational, privacy-safe identity across all brands: probabilistic ID resolution that resolves unauthenticated signals to households/persons with calibrated confidence (entity resolution with gradient boosting and embeddings, representation learning, isotonic calibration, candidate blocking, champion/challenger pipelines), expanding addressable audiences beyond deterministic matching.
• Audience Intelligence — advertising and marketing use cases: lookalike and predictive audiences (LAL across 1,000+ features), ML-driven smart audiences, layered retrieval + propensity, and incrementality/closed-loop optimization, with privacy-safe activation including data clean rooms.
• ML-based Forecasting — audience growth, demand, and advertising yield/pricing forecasting that powers ad sales and marketing decisions.
• Content Preferences & Affinity — genre-preference, content-preference, and single-title affinity modeling (two-tower retrieval with semantic content embeddings) that ranks audiences for upcoming titles and powers cross-channel promotion.

What You’ll Bring

Required

• 5–8 years of industry experience in ML engineering or applied data science (3+ years with a Ph.D.).
• Deep Python expertise and production-quality software engineering practices; production experience building and deploying ML at scale (millions+ of users/records).
• Strong proficiency in Databricks (PySpark, Delta Lake, Workflows/DLT, MLflow, Unity Catalog) and solid SQL/Snowflake experience for feature sourcing and model-output delivery.
• Experience with AWS ML services (SageMaker, S3, Lambda).
• Strong understanding of ML model evaluation, A/B testing, and statistical inference; knowledge in one or more of recommendations & ranking, identity resolution, embeddings/retrieval, causal/interpretable ML, forecasting, bandits, or optimization.
• Demonstrated ability to lead technical decisions and mentor engineers.
• Bachelor’s or Master’s degree in Computer Science, Statistics, Engineering, or a related quantitative field (or equivalent experience).
• Excellent written and verbal communication, with the ability to advocate technical solutions to engineers, scientists, and product stakeholders.
  

Preferred:

• Recommendation systems, personalization, identity resolution, or audience modeling in a media / streaming / ad-tech context.
• Familiarity with Data Clean Room environments (Snowflake DCR, AWS Clean Rooms) and consent-aware activation.
• Experience with two-tower / retrieval architectures, probabilistic identity resolution (graph-based matching, entity resolution), and probability calibration.
• Hands-on experience with agentic AI frameworks (LangChain, LangGraph, AutoGen, MCP), Databricks Genie Space configuration, and Snowflake Cortex.
• Experience with feature stores (Databricks Feature Store, Tecton, Feast) and contributions to open source or ML publications.

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.