Warner Bros. Discovery

Machine Learning Engineer

• Lead the design and development of large-scale model driven search algorithms including retrieval, ranking, and query understanding
• Define and evolve the technical strategy for search, balancing relevance, latency, and scalability
• Drive end-to-end ML systems: data pipelines, feature engineering, model training, experimentation, and serving
• Partner closely with product, data, and infrastructure teams to shape and execute on the search roadmap and user experience
• Mentor engineers and data scientists and raise the bar on algorithm development, system design, ML and engineering practices
• Identify and drive high-impact opportunities across search and personalization
• Motivate, inspire and create a culture of experimentation and data-driven innovation

Staff Machine Learning Engineer – Search

Data Scientist

• Operate at the highest technical individual-contributor level in Data Science, Machine Learning, and Applied AI
• Design, architect, and deliver complex and business-critical AI systems
• Lead the end-to-end design and implementation of advanced ML systems
• Own and deliver mission-critical AI solutions
• Serve as a trusted technical advisor to senior leaders across various teams
• Mentor senior and staff-level data scientists through technical guidance
• Contribute to internal AI communities of practice, technical forums, and standards bodies

Senior Principal Data Scientist

Product Designer

• Spearhead the design and implementation of compelling activities to entertain and challenge players
• Collaborate closely with the Game Director and Design Director to create a player-driven open world
• Articulate a compelling vision for gameplay activities and get buy-in from stakeholders
• Maintain useful and accurate documentation of designs
• Work in tandem with the Lead World Designer to integrate game activities seamlessly into the level design
• Take feedback and direction from the Design Director
• Balance the needs of design with those of art, engineering, and production
• Build and lead a world-class team of activity designers
• Mentor and manage the career growth of more junior activity designers
• Conduct regular playtests of game activities

Lead Designer, Activity

• Directly manage 7 ICs (2× MLE 2, 3× Senior MLE, 1× Staff MLE, 1× Staff DS); own their performance, growth, and career development.
• Establish a high-performance engineering culture: technical excellence, ownership, psychological safety, rigorous experimentation, and continuous learning.
• Maintain sufficient technical depth to review ML architecture proposals, challenge design decisions, and unblock technical escalations across all workstreams.
• Own end-to-end delivery accountability for Hyderabad’s ML projects: scope, milestones, dependencies, risks, and stakeholder communication.
• Overseer ML Promo Optimizer, STAT v2, and segmentation that power advertising and marketing activation.
• Serve as the primary point of contact for the Hyderabad ML team across Product, Marketing, Ad Sales, Data Engineering, Legal/Privacy, and Finance.

Senior Manager, Machine Learning Engineering, Data & Audience Platform

• Define and own the technical architecture for the team’s core systems: the probabilistic identity spine, audience intelligence platform, content-affinity and genre-preference models, and ML-based forecasting
• Lead architectural decisions for the team’s MLOps framework — feature-store design, training-pipeline standards, model-serving patterns, and monitoring infrastructure — on a Databricks-first architecture, integrating Snowflake and AWS SageMaker where each is the right tool
• Architect and lead delivery of the probabilistic identity resolution system — resolving unauthenticated device IDs and 1P cookies to households/persons with calibrated confidence at scale across all WBD brands — using entity resolution, embeddings/representation learning, calibration, candidate blocking, and champion/challenger promotion
• Define the team’s MLOps target architecture: feature contracts, model-registry governance, automated retraining, drift detection, and A/B experimentation infrastructure
• Lead the team’s adoption of agentic AI development: define standards for using Cursor, GitHub Copilot, and Amazon Q in production ML workflows, and for MCP-based tooling

Staff Machine Learning Engineer, Data & Audience Platform

• 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, single-title affinity, and audience/propensity models.
• Design scalable feature pipelines on Databricks and the WBD 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.
• Develop and optimize models across the ML spectrum: gradient boosting, embedding/two-tower retrieval, neural ranking, probability calibration, and probabilistic/graph-based matching.
• Design rigorous offline and online experiments; define evaluation frameworks appropriate to each use case.
• Contribute to lookalike modeling using 1,000+ first- and third-party features.
• Champion MLOps best practices: model versioning, automated retraining triggers, drift detection, and production monitoring with MLflow.
• Build and maintain robust, reproducible, auditable ML pipelines on Databricks and enforce leakage prevention and training/serving consistency.
• Mentor MLE 2s through code reviews, design discussions, and pairing.

Senior Machine Learning Engineer, Data & Audience Platform Team

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

Machine Learning Engineer II, Data & Audience Platform Team

• Lead end-to-end design and delivery of production-grade Gen AI based Media processing systems that supports language localization capabilities like Closed Captioning, Language translation, or Dubbing.
• Lead data science experimentation by designing experiments, defining success metrics, iterating on model improvements, and transitioning validated approaches into scalable production ML pipelines.
• Establish and operate robust production evaluation frameworks, covering model quality, drift detection, inference performance, and system-level metrics with and without ground truth.
• Lead, mentor, and grow a team of Applied data scientist, Machine Learning Engineers, setting clear technical direction, delivery standards, and career development paths.
• Motivate, inspire and create a culture of experimentation and data-driven innovation while constantly striving to be an advocate for doing what is right for our customers

Senior Staff Data Scientist

• The Staff Data Scientist will serve as a individual contributor driving the design, experimentation, and delivery of advanced machine learning solutions for media intelligence and localization use cases.
• This role defines modeling approaches that advance the state of the art, and turns research into scalable, production-ready ML systems with measurable product and customer impact.
• Lead end-to-end design and delivery of production-grade Gen AI based Media processing systems that supports language localization capabilities like Closed Captioning, Language translation, or Dubbing.
• Lead data science experimentation by designing experiments, defining success metrics, iterating on model improvements, and transitioning validated approaches into scalable production ML pipelines.
• Establish and operate robust production evaluation frameworks, covering model quality, drift detection, inference performance, and system-level metrics with and without ground truth.
• Lead, mentor, and grow a team of Applied data scientist, Machine Learning Engineers, setting clear technical direction, delivery standards, and career development paths.
• Motivate, inspire and create a culture of experimentation and data-driven innovation while constantly striving to be an advocate for doing what is right for our customers

Staff Data Scientist

SDET

• Scope and Complexity: Acquired subject-matter expertise over one or more production software systems
• Decision Making: Solve business problems with simple solutions, applying appropriate technologies
• Execution: Built multiple stable, scalable feature changes successfully shipped to customers
• Operations: Exemplary work driving best practices and setting standards
• Influence: Collaborative leader mentoring other engineers
• Communication: Effective communicator with stakeholders and adjacent teams

Staff Software Engineer in Test, Sports Team

Full-stack Engineer

• Build engaging experiences to create a direct relationship with users
• Collaborate with cross-functional teams to create secure, exciting, and performant experiences
• Develop and test products that drive customer acquisition and engagement
• Maintain and develop components using modern JavaScript
• Optimize code for performance and scalability
• Ensure cross-browser compatibility and responsiveness of the application

Senior Software Engineer, Growth Team

• You'll be part of the larger Operational Engineering group responsible for building a truly global, self-service cloud platform (PaaS)
• Describe your design, code, and architectural decisions to your team
• Actively seek input where appropriate and engage your team to inform and share what you are doing
• Embrace constructive feedback and adapt to align with organizational standards and objectives
• Offer enhancements to best practices, document, design, and architecture improvements, and add feedback through code reviews to other engineers.

Software Engineer II – Cloud Support and Operations – AWS

Financial Planning and Analysis

• support month-end close from an FP&A perspective, including OPEX P&L variance analysis and review of actual spend with business partners and budget holders
• summarise key financial insights to support decision-making and improve business unit and regional profitability
• contribute to monthly cost forecasting, including preparation, system updates, and ongoing tracking of financial performance
• support the annual budgeting process, including template design, review of planning assumptions, and development of business cases
• provide accurate, timely and relevant reporting, ensuring clear interpretation and communication of financial results
• partner with business stakeholders on day-to-day finance needs and actively participate in cross-functional interactions
• drive continuous improvement of FP&A processes and reporting, including ad hoc analysis and cost management support

Senior FP&A Opex Analyst

Analyst

• Conduct analyses for the Global Insights team on Brazil’s basic brands
• Provide ad hoc support to Brazil teams across adult basic brands, including programming, communications, marketing, and related areas, across both Pay TV and platforms such as YouTube
• Update and maintain reports for WBD brands
• Support ad hoc studies by providing and preparing relevant data
• Support in LATAM analyses
• Assisting in meetings with content teams areas
• Participating in the step-by-step process of behavioral studies

Junior Analyst, Data & Insights

Producer

• Support the end-to-end management of original production projects in LATAM
• Provide operational support to production companies throughout the full project lifecycle
• Track deliverables and milestones, review materials with internal stakeholders
• Ensure required information is updated in Seenchronize
• Maintain and organize up-to-date project information across shared platforms (Teams, Monday, internal folders, etc.)
• Ensure the use of standardized internal tools, systems, and templates by internal production teams and external production companies
• Support financial processes, including cost tracking and invoice supervision
• Coordinate post-production workflows, including Frame.io access management, review cycles, delivery tracking, and material distribution