Netflix is looking to leverage GenAI to drive differentiated value for its Ads business, improve creative asset generation and understanding, and unlock new opportunities for innovation and efficiency within its advertising systems.
Requirements
- Deep expertise in Generative AI, including diffusion models for image and video generation, vision-language models, and multimodal semantic understanding models such as multimodal transformers.
- Experience with fine-tuning techniques (such as LoRAs and adapters), developing evaluation frameworks (including human-in-the-loop quality assessments), and working with large language models (LLMs) for tasks like tool integration and summarization.
- Experience with multimodal machine learning (vision, language, audio)
- Proven ability to prototype solutions and work cross-functionally to enable the design and scaling of modular ML systems
Responsibilities
- Set the strategic direction for Netflix's GenAI investment portfolio in ads and own the overall outcomes for investments in GenAI for Ads.
- Design and architect modular, scalable systems for creative asset generation (e.g., images, video, audio) and creative understanding (e.g., semantic analysis, content summarization) that integrate seamlessly into Netflix’s advertising ecosystem.
- Lead hands-on prototyping and experimentation with state-of-the-art generative AI models (e.g., diffusion models, large language models, multimodal transformers - across both open source and proprietary tech).
- Collaborate closely with Netflix’s Product, Engineering, and Data Science teams to deeply understand creative and advertising needs, ensuring solutions are both technically robust and creatively inspiring.
- Oversee integration of generative AI systems with Netflix’s existing ad tech stack, creative tooling, and data infrastructure.
- Mentor and guide other engineers and scientists in generative AI best practices, fostering a culture of technical excellence and creativity
Other
- Effective cross-functional collaborator with creative and technical teams
- Ability to influence and mentor without direct authority
- Commitment to quality, ethics, and brand safety in AI solutions
- Data-driven, with experience defining and tracking success metrics
- Passion for continuous learning and thought leadership in AI
