Welcome to Warner Bros. Discovery… the stuff dreams are made of.
Who We Are…
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.
Your New Role:
As a Senior Machine Learning Engineer in our AI Center of Excellence (CoE), you will be a key player in driving the next wave of AI innovation within one of the largest and most dynamic Media & Entertainment companies in the world. You will work on cutting-edge technologies that power personalized content, enhance user experiences, and shape the future of media consumption.
This is a rare opportunity to leverage your expertise in machine learning to solve complex, large-scale challenges and contribute to the development of AI systems that reach millions of users globally.
You will collaborate closely with cross-functional teams, including data scientists, engineers, and product managers, to deliver high-performance machine learning models.
These models will help to optimize content recommendations, enhance media production processes, and innovate in areas like real-time content analysis, customer personalization, and predictive analytics. Your work will directly impact the company’s ability to maintain its leadership position in the media industry.
Your Role Accountabilities:
1. Model Development & Deployment
Lead the design, development, and deployment of robust machine learning models for real-time systems and large-scale data applications.
Develop and optimize algorithms for content recommendation, user behavior prediction, and personalized experiences across various platforms.
Collaborate with the data engineering team to integrate machine learning models into the production environment, ensuring scalability and performance.
Conduct A/B testing and model evaluations to ensure the continuous improvement of deployed systems.
Ensure the models are optimized for latency, throughput, and resource utilization in a high-performance, production environment.
2. Research & Innovation
Stay on top of the latest advancements in machine learning, deep learning, and AI to apply new methods and techniques to business problems.
Lead research efforts into new algorithms and solutions to drive AI innovation in areas such as NLP, computer vision, and recommendation systems.
Collaborate with academic institutions and industry experts to bring cutting-edge technologies into the organization.
Push the boundaries of AI by experimenting with generative models, reinforcement learning, and other advanced machine learning techniques.
Foster a culture of innovation within the team by encouraging experimentation and creative problem-solving.
3. Team Leadership & Collaboration
Mentor junior engineers and data scientists, providing guidance on best practices in machine learning model development and deployment.
Work closely with product managers and engineers to define project goals, timelines, and deliverables, ensuring alignment with business objectives.
Contribute to the AI CoE’s strategic direction, helping to define priorities and the roadmap for AI innovations across the organization.
Lead technical discussions and collaborate with other engineering teams to integrate AI models into broader system architectures.
Conduct knowledge-sharing sessions and internal workshops to elevate the technical capabilities of the team.
4. Data & Model Management
Oversee data preprocessing, feature engineering, and data pipeline development to ensure high-quality data for model training.
Ensure that data used for model training and evaluation is cleaned, structured, and optimized for machine learning processes.
Manage the lifecycle of machine learning models, including version control, monitoring, and retraining based on performance metrics.
Implement effective model monitoring and diagnostics to quickly identify and address model drifts or issues in production systems.
Work with the data governance team to ensure compliance with data privacy regulations and ethical standards in AI model development.
5. Performance Optimization & Scalability
Optimize machine learning models for scalability and efficiency in cloud environments (AWS, GCP, Azure).
Focus on building solutions that are designed for high availability and performance, ensuring models are highly reliable under heavy traffic loads.
Continuously monitor model performance and identify opportunities for optimization in terms of speed, accuracy, and resource consumption.
Work with DevOps to streamline deployment processes and reduce model deployment times.
Utilize automated tools to monitor and track model performance, ensuring high-quality outputs in production.
Qualifications & Experiences:
Master’s or Ph.D. in Machine Learning, Artificial Intelligence, or a related discipline.
5+ years of experience in machine learning, with a proven track record of deploying models at scale.
Strong knowledge of deep learning techniques, including CNNs, RNNs, and transformer-based models, with practical experience in NLP, computer vision, or reinforcement learning.
Proficiency in programming languages such as Python, Java, and C++, with a strong emphasis on Python for ML workflows.
Strong knowledge of ML lifecycle management tools like MLflow or SageMaker.
Excellent communication skills with a passion for mentoring.
Strong programming skills in Python and experience with relevant libraries (e.g., TensorFlow, PyTorch, scikit-learn).
Experience with cloud computing platforms (e.g., AWS, GCP, Azure) and their machine learning services.
Strong understanding of machine learning algorithms and techniques, including deep learning, natural language processing, and computer vision.
Excellent problem-solving, analytical, and communication skills.
Experience with agile development methodologies and continuous integration/continuous delivery (CI/CD) pipelines.
A strong publication record in top-tier conferences or journals (preferred).
How We Get Things Done…
This last bit is probably the most important! Here at WBD, our guiding principles are the core values by which we operate and are central to how we get things done. You can find them at www.wbd.com/guiding-principles/ along with some insights from the team on what they mean and how they show up in their day to day. We hope they resonate with you and look forward to discussing them during your interview.
Championing Inclusion at WBD
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.