Senior Research Scientist, Search Ads Quality

Google•Apply•Published 4 hours ago•First seen 4 hours ago

As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more.

As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

In this role, the Ads AI team in Search Ads Quality is seeking a Research Scientist (RS) or Software Engineers (SWE) to develop Artificial Intelligence (AI) solutions including Large Language Models (LLMs), Agentic AI, and multimodal modeling for critical search problems. We bridge the gap between research and product, delivering launches while publishing in conferences. You will need to have proficiency in C++ and Python and experience in Ads or AI. We operate as a startup within Google, and offer a collaborative environment with significant opportunities for technical growth and leadership.

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.

The US base salary range for this full-time position is $166,000-$244,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.

Responsibilities

Solve Ads issues while maintaining high-quality and low-latency performance standards.
Collaborate with Ads and Research teams to design and implement solutions.
Utilize various AI technologies, including Agentic AI, LLM fine-tuning, and Large Embedding Model (LEM) optimization.
Work alongside AI/ML experts from various fields to address critical advertising problems.
Publish novel research and share technical insights with the global academic community.

Minimum qualifications:

PhD degree in Computer Science, a related field, or equivalent practical experience.
2 years of experience leading a research agenda.
Experience in one or more areas of Machine Learning, such as AI algorithms, AI platforms or Generative AI Agents.
One or more scientific publication submissions for conferences, journals, or public repositories (such as NeurIPS, ICML, ICLR, COLM, ACL, EMNLP, NAACL, CVPR, etc.).

Preferred qualifications:

Experience with software development in a practical setting.
Experience in statistical modeling or ML or mathematical modeling in industry or in academia.
Experience with TensorFlow, Adbrain, Flume, Keras.
Experience with Ads (auction theory, etc.).
Knowledge of data analysis.