Minimum qualifications:

　　15 years of experience in software development, or 10 years with an advanced degree in Computer Science, Mathematics, Statistics, or Engineering.

　　5 years of experience in driving technical collaboration across multiple teams and organizations.

　　Experience building and improving Information Retrieval and Ranking, Machine Learning, Embedding systems. Preferred qualifications: Experience in indexing, retrieval, and ranking stacks.

　　Understanding of LLM fine-tuning, distillation, data generation, and prompt engineering across a large variety of tasks.

　　Ability to work cross-functionally, partnering with groups (e.g., Sales, Engineering, Product Management, Product Marketing, UX and UI), and brokering trade-offs with stakeholders and understanding their needs.

　　Outstanding narrative and storytelling skills that propel usage, adoption, and market momentum.

　　About the job

　　Thanks to years of innovation in Search, "google" has become a verb that simply means search. Advancements in LLM and generative technologies have improved the search experience, as demonstrated by new summarization and multi-turn search capabilities. Yet not every search happens at google.com -many other websites and desktop and mobile applications offer search as an integral part of their user experience.

　　We have a unique opportunity to transform this market, enabled by Google's differentiation in search and generative AI. Google Cloud AI has responded with Vertex AI Search, a platform and solution suite with the mission to provide Google quality search and generative capability as a cloud service.

　　Vertex AI Search empowers enterprise developers to quickly ingest their data through APIs and seamlessly integrates advanced AI powered search capabilities out of the box, including semantic retrieval and ranking, generative summarization, and multi-turn search. We seek to empower enterprises to build their own search experiences by offering differentiated Vertex AI Search platform components, such as Vector Search, Document Understanding, and more.

　　As a Principal Engineer, you will drive the technical strategy, architectural design, and execution of quality in Vertex AI Search. You will utilize your technical expertise to lead the development of retrieval and ranking systems.You will have proficiency in applying machine learning and embedding technologies to improve ranking quality and a passion to innovate LLM to advance search result summarization and multi-turn conversational search.

　　Google Cloud accelerates organizations' ability to digitally transform their business with the best infrastructure, platform, industry solutions and expertise. We deliver enterprise-grade solutions that leverage Google's cutting-edge technology - all on the cleanest cloud in the industry. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.

　　The US base salary range for this full-time position is $271,000-$399,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. 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

　　Develop a strategic outlook for the Cloud AI organization to innovate high quality and scalable retrieval and ranking solutions.

　　Design, build, and deploy search and summarization solutions which leverage the latest LLM technologies to improve relevance ranking, grounding factuality, and summarization and conversation quality.

　　Drive the latency, robustness, and scalability of the ranking system, and provide guidance to improve retrieval and ranking latency and throughput by making best use of Google's GPU/TPU and Machine Learning infrastructure.

　　Establish an innovative product and research agenda. Initiate and lead collaborations across product areas to advance search quality.

　　Advise on A/B testing and experimental frameworks. Drive the adoption and building of comprehensive benchmarks to quantify our search quality, gap, and differentiation.