59 Youtube Data Jobs Hiring Near You

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.
• 7 years of experience leading technical project strategy, ML design, and working with ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience with design and architecture; and testing/launching software products.
• 5 years of experience with one or more of the following: generative AI, deep learning, reinforcement learning, or specialization in another machine learning field.

Preferred qualifications:

• Master's degree or PhD in Engineering, Computer Science, or a related technical field.
• 5 years of experience in a technical leadership role leading project teams and setting technical direction.
• 3 years of experience working in a complex, matrixed organization involving cross-functional, or cross-business projects.
• Experience building and productionizing machine learning models at scale, using frameworks (e.g., TensorFlow, JAX) and production platforms.
• Familiarity with online advertising systems, creative optimization, personalization, or recommender systems.

About the job
Google's software engineers develop the next-generation technologies that change how billions of users connect, explore, and interact with information and one another. Our products need to handle information at massive scale, and extend well beyond web search. We're looking for engineers who bring fresh ideas from all areas, including information retrieval, distributed computing, large-scale system design, networking and data storage, security, artificial intelligence, natural language processing, UI design and mobile; the list goes on and is growing every day. As a software engineer, you will work on a specific project critical to Google's needs with opportunities to switch teams and projects as you and our fast-paced business grow and evolve. We need our engineers to be versatile, display leadership qualities and be enthusiastic to take on new problems across the full-stack as we continue to push technology forward.
As a Senior Staff Generative AI Engineer, you will be a technical leader driving the architectural outlook, data engine, and evaluation frameworks for core foundation models powering ad creative intelligence. You will advocate efforts to build frontier-grade pre-training and post-training datasets, as well as automated evaluation systems to accelerate safe and high-performing model deployment at scale. Your work will involve handling challenging problems in foundation data curation and pipeline scaling, evaluation frameworks and benchmarking, model alignment and post-training, exploration and quality metrics, and unified intelligence architecture.
Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We're made up of multiple teams, building Google's Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $262000 - $364000 (USD) 25% 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 AI, from concept to production.
• Define the technical strategy and roadmap for key machine learning components, ensuring alignment with product goals and driving measurable impact through rigorous experimentation.
• Build and integrate scalable machine learning pipelines and services, collaborating with infrastructure and serving teams to power creative optimization.
• Analyze performance metrics, derive insights, and implement algorithmic improvements for creative selection, freshness, and exploration.
• Mentor engineers and promote best practices in applied machine learning and system design.

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