Description

Minimum qualifications:

• Bachelor's degree or equivalent practical experience.
• 5 years of experience programming in C and SQL.
• 3 years of experience with one or more of the following: reinforcement learning (e.g., sequential decision making), recommendations/ranking, LLMs, ML infrastructure, or specialization in another ML field.
• 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.

Preferred qualifications:

• Experience with working on Ads or product quality improvement areas.
• Experience with integrating new machine learning research techniques.
• Experience with working on ranking and retrieval models.

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.

In this role, you will build the next generation of modeling and quality infrastructure for queryless ad formats-a complex space where user intent is implicit rather than stated. You will lead technical roadmaps across retrieval, auction, and measurement, utilizing techniques such as LLM-based distillation and differential modeling.

As a technical leader, you will work separately to identify new opportunities, driving quality and business improvements across the entire stack while collaborating with cross-organizational teams in Organic Maps and Personalization. This is a unique opportunity to use advanced AI to shape the future of local discovery at scale and triple our impact over the next five years.
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 $174,000-$252,000 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

• Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
• Move to high-performance ML models utilizing factorization for sub-millisecond relevance optimization.
• Build a new pRelevance model that incorporates deep personalization signals through non-traditional techniques like differential modeling and transfer learning.
• Leverage Large Language Model (LLM) based distillation to teach models what is relevant in scenarios where manual dataset creation is unfeasible.
• Develop evaluation frameworks where LLMs simulate user personas to predict true ad quality.

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Requisition #: 80879617878958790

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