Senior Machine Learning Engineer

Welcome to Warner Bros. Discovery… the stuff dreams are made of.

When we say, “the stuff dreams are made of,” we’re not just referring to the world of wizards, dragons and superheroes, or even to the wonders of Planet Earth. Behind WBD’s vast portfolio of iconic content and beloved brands, are the storytellers bringing our characters to life, the creators bringing them to your living rooms and the dreamers creating what’s next…

From brilliant creatives, to technology trailblazers, across the globe, WBD offers career defining opportunities, thoughtfully curated benefits, and the tools to explore and grow into your best selves. Here you are supported, here you are celebrated, here you can thrive.

We are seeking a Senior Machine Learning Engineer to lead the development and deployment of scalable machine learning solutions for the Audience Intelligence team. You will work on projects such as building scalable lookalike and predictive audiences, user behavior modeling, ML forecasting systems, ML-based s, and developing ML solutions to better understand HBO Max user bases. This role blends deep technical expertise, research rigor, and production‑grade engineering practices.

You will mentor junior engineers and scientists, collaborate cross‑functionally, and advance our ML capabilities through innovation and thought leadership.

• Model Development: Design and implement machine learning models for audience intelligence applications, including lookalike and predictive audience generation, user behavior modeling, ML forecasting, ML-based , and ML-based user base characterization for HBO Max. Stay current with ML research, explore new methodologies, and contribute to the company’s research agenda through publications and open‑source contributions when possible.

• End‑to‑End Model Delivery: Architect, build, and maintain complete ML pipelines from data ingestion and processing to model deployment and monitoring in production environments, ensuring robust delivery and operational excellence.

• Infrastructure & Automation: Develop robust, scalable infrastructure and automated workflows for ML training, evaluation, and deployment, using best practices in MLOps.

• Cross‑Functional
  
  Collaboration:
  
  Work closely with product, engineering, and business stakeholders to translate requirements into effective ML solutions.

• Mentorship: Guide and mentor junior engineers and scientists, fostering a culture of knowledge sharing and technical excellence.

• Production Operations: Ensure robust model deployment, monitoring, and optimization to meet business requirements for latency, concurrency, and reliability.

• MLOps Governance & Compliance: Promote best practices around MLOps among broader data science groups to ensure consistent model quality and faster deployment cycles. Ensure end‑to‑end governance and compliance for ML systems by enabling model traceability (e.g., audit trails for model changes), enforcing data privacy standards and meeting regulatory requirements relevant to our industry.

• Education: PhD or MSc in Machine Learning, Computer Science, Data Science, or a closely related field.

• Experience: 3+ years of hands‑on experience in machine learning engineering, including model development and production deployment.

• Technical
  
  Skills:
  
  

• Proficient in Python and ML frameworks such as PyTorch, Tensor Flow, MLflow, and Sage Maker.

• Expert on SQL and large‑scale data processing languages.

• Proven track record of deploying ML models to production.

• Familiarity with distributed computing systems (Spark, EMR).

• Experience with cloud platforms (AWS, Databricks, Snowflake).

• Strong computer science fundamentals, including data structures, algorithms, and system design.

• Mindset: Strong desire to learn new techniques and adapt to evolving business needs.

• Communication: Ability to clearly articulate ML concepts to diverse stakeholders.

• Experience in AI/ML advertising solutions, marketing sciences, recommendations, search, ranking and personalization systems.

• Publications in top tier ML conferences or contributions…