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.
With 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 lead efforts, address foundational, open-ended problems in partnership with teams across Ads and DeepMind, and leverage AI to build 10x better ads experiences.
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 $207,000-$300,000 + 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
- Optimize the Search results page (including organic results and ads) to co-evolve ads, leverage AI to maintain business generation, create ad experiences, help users find content faster, and enable growth opportunities for advertisers.
- Leverage AI/ML advancements, including Large Language Models (LLMs) and Agentic AI, to solve optimization problems across search ads, from product innovation to agentic automation.
- Deliver ad experiences in the world of AI.
- Design, build, and maintain self-service tools and platform features that embed AI/ML quality evaluation methods.
- Architect systems for deploying and serving AI/ML models specifically for data quality assessment tasks.
Qualifications
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 8 years of experience with one or more general purpose programming languages including but not limited to: Java, C/C++ or Python.
- 5 years of experience with ML design and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
- Experience with multimodal machine learning or LLMs, including building and launching products.
- Experience building, experimenting, and deploying AI-first systems and processes into production.
Preferred qualifications:
- Experience in applied AI/ML, including model development, fine-tuning, application and evaluation.
- Experience leveraging Generative AI/LLMs for user-facing problems.
- Experience designing and building full-stack systems and self-service platforms.
- Experience with search/ads or quality systems.
- Familiarity with the latest research and trends in AI/ML, particularly as applicable to data quality and assessment.
- Understanding of GenAI data, evaluation methodologies, and data quality best practices.