The role
From Google's own posting.
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.
The team leverages AI-generated content across major surfaces, ensuring optimal user experience at a massive scale. In this role, you will focus on end-to-end personalization and system optimization, integrating feedback loop signals to enhance model performance for over 4 billion users.
In Google Search, we're reimagining what it means to search for information – any way and anywhere. To do that, we need to solve complex engineering challenges and expand our infrastructure, while maintaining a universally accessible and useful experience that people around the world rely on. In joining the Search team, you'll have an opportunity to make an impact on billions of people globally.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 .
Lead the development of advanced AI-generated content (AIGC) recommendation frameworks and agentic flows to generate high-quality content for Discover and Notifications.
Build and implement advanced offline and online quality evaluation frameworks for LLMs to ensure content factuality, freshness, and coherence.
Conduct advanced quality evaluations and leverage dense recommendation signals and feedback to improve the overall performance of the AIGC stack.
Drive end-to-end modeling for the personalization flow, focusing heavily on inventory quality, retrieval, ranking, user understanding, and budgeting predictions.
Optimize ML inference and resolve system-level bottlenecks to improve serving efficiency for low-latency, high queries-per-second (QPS) global surfaces.
Minimum qualifications:
Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.
8 years of experience in software development.
7 years of experience leading technical project strategy, ML design, and working with industry-scale ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
Experience building quality evaluation frameworks for large language models.
Experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production.
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.
Experience building advanced offline and online quality evaluation frameworks for Large Language Models (LLMs).
Experience integrating advanced quality evaluation frameworks with downstream recommendation signals.
Experience mentoring executive engineers and influencing broader organizational machine learning strategies.
Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.
8 years of experience in software development.
7 years of experience leading technical project strategy, ML design, and working with industry-scale ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
Experience building quality evaluation frameworks for large language models.
Experience building and deploying recommendation systems models (retrieval, prediction, ranking, personalization, search quality, embedding) in production.