8+ yrsBengaluruOn-siteDirect career pageEst. 45–85 LPA
Posted 14h ago
Reach people at Warner Bros. (Warner Bros. Entertainment Inc.)
People at this company you can reach
Log in to see who works here
Create a free account or log in to see the people at Warner Bros. (Warner Bros. Entertainment Inc.) you could write to:
- Recruiters & HR
- People in this function
- Work addresses
Your match
Against your primary resume
Log in to see your match score
Create a free account or log in to unlock your CV match score across:
- Role fit
- Experience
- Industry relevance
Protocol Intelligence
How competitive this role is
Competition
Background fit
Strictness
Log in to see why each signal reads the way it does.
Job description
Structured overview of role & requirements
About This Role
- Lead design and delivery of production-grade Generative AI based media processing systems enabling language localization such as closed captioning, translation, and dubbing.
- Own data science experimentation including designing experiments, defining success metrics, iterating model improvements, and deploying validated ML pipelines at scale.
- Establish and operate production evaluation frameworks for model quality, drift detection, inference performance, and system-level metrics; lead and mentor applied data scientists and ML engineers setting technical direction and standards.
Minimum Requirements
- 8+ years of experience building production-grade machine learning solutions.
- Experience designing machine learning-driven, large-scale distributed systems serving a large customer base.
- Practical knowledge of modern machine learning lifecycle and platform concepts.
- Preferred advanced degree (Master's or PhD) in Computer Science, Machine Learning, or related field; 3+ years hands-on experience in Computer Vision, ASR, Translation, or Text-to-Speech technologies.
Ideal Candidate Profile
- Proven ability to transition ML research into scalable, production-ready systems impacting media intelligence and localization.
- Experienced in leading cross-disciplinary teams and driving data-informed, experimental innovation at scale.
- Senior-level expertise with a strong background in designing and managing large scale ML infrastructures and collaborating effectively with global teams.