Minimum qualifications:- Bachelor's degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience testing and launching software products, and 3 years of experience with software design and architecture.
Preferred qualifications:- Master's degree or PhD in Computer Science, Machine Learning, or a related technical field.
- 8 years of experience in data structures and algorithms.
- 5 years of experience building and productionizing machine learning models, with experience in deep learning or reinforcement learning.
- Experience with machine learning frameworks like TensorFlow and JAX, production machine learning platforms such as TensorFlow Extended and AdBrain, and generative models and their applications.
- Proficiency in designing, running, and analyzing large-scale online experiments (A/B tests).
- Familiarity with online advertising systems, creative optimization, personalization, or recommender systems.
About the jobWith your technical expertise you will manage project priorities, deadlines, and deliverables. You will design, develop, test, deploy, maintain, and enhance software solutions.
In this role, you will focus on revolutionizing how ad creatives are composed, personalized, and optimized at scale using artificial intelligence/machine learning techniques. You will develop intelligent systems that enhance the relevance and performance of Ad creatives served to billions of YouTube users. Your work spans deep learning, reinforcement learning, generative artificial intelligence, and large-scale serving platforms to power the next generation of ad creative experiences.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $301000 (USD) 20% bonus target equity benefits
Learn more about benefits at Google .
Responsibilities - Lead the end-to-end development of novel machine learning models, incorporating techniques like deep learning, reinforcement learning, and generative artificial intelligence, from concept to production.
- Build and scale end-to-end machine earning pipelines for model training, inference, and integration with high-throughput Ad serving systems.
- Explore, implement, and integrate with generative models for text, image, and video adaptations within Ads.
- Apply deep learning and reinforcement learning to understand asset value and optimize creative composition while developing metrics and algorithms to ensure creative freshness and efficient exploration.
- Contribute to the architecture of centralized services for unifying asset attributes and model-driven insights across different applications, collaborating with infrastructure and serving teams to power creative optimization.
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