Director-Machine Learning Engineering

3 days ago

Bengaluru, Karnataka, India Warner Bros. Discovery Full time
Job Description

Roles and responsibilities

- The role will focus on building and leading a team responsible for unlocking machine learning opportunities in media supply chain, captioning services, ad conditioning, metadata extraction and building foundational machine learning training and inference pipelines at scale. You will lead by example and define the best practices, will set high standards for the entire team and for the rest of the organization. You have a successful track record for ambitious projects across cross-functional teams. You are passionate and results oriented. You strive for technical excellence and are very hands-on. Your co-workers love working with you. You have built respect in your career through concrete accomplishments.
- Responsible for machine learning and data science development for IDC.
- Recruit and build out teams for applied machine learning org.
- Build cutting-edge capabilities utilizing machine learning and data science (e.g., large language models, computer vision models, advanced ad & content targeting, etc.)
- Mentor, guide, coach, coordinate training opportunities and support individual growth and technical skills across the engineering teams.
- Enable continuous learning environment to keep abreast of industry trends. Partner with research organizations and explore innovation opportunities.
- Leverage industry best practices and tools to continually improve teams ability to build, operate and maintain products.
- Ensure that technical solutions are in line with established WBD Digital strategy, standards in respect to architecture, security, corporate governance, coding standards, monitoring, logging, unit test, and service enablement.

What to Bring

- 18+ years of experience managing Machine Learning teams.
- years of experience designing, building, deploying, testing, maintaining, monitoring, and owning scalable, resilient, and distributed machine learning algorithms and systems.
- Proficiency in operating machine learning solutions at scale, covering the end-to-end ML workflow.
- Experience in one or more of Deep Learning, Reinforcement Learning, Causal Inference.
- Familiarity with real-world ML systems (configuration, data collection, data verification, feature extraction, resource and process management, analytics, training, serving, validation, experimentation, monitoring).
- Obsession for service observability, instrumentation, monitoring, and alerting.
- Knowledge of AWS, GCP or similar cloud platforms.
- PhD or masters in computer science or related discipline.

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