Software Engineer, Search AI, Search Quality

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with programming in Python and C++, or 1 year of experience with an advanced degree.
• 2 years of experience in large-scale distributed data processing.
• 1 year of experience with Natural Language Processing (NLP) concepts or techniques.
• 1 year of experience with Machine Learning, Deep Learning.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical fields.
• 2 years of experience working in an organization involving cross-functional, or cross-business projects.
• Experience in Search Quality and Machine Learning.
• Experience with JAX or TensorFlow.
• Understanding of Applied deep learning with large language models, including with prompting, Reinforcement Learning from Human Feedback (RLHF) and supervised fine-tuning.

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.

Join a transformative project to overhaul the Google Search stack, ensuring Large Language Models (LLMs) and machine learning are core components.

In this role, this position requires close partnership with various engineering teams, including those working on embedding Retrieval Engine (RE) and other key Search systems. You'll be at the forefront of applying AI research to enhance products used by many users contributing significantly to both AI advancements and the new Search architecture.

In Google Search, we're reimagining what it means to search for information – any way and anywhere. To do that, we need to solve 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.
The US base salary range for this full-time position is $141,000-$202,000 + 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.
The US base salary range for this full-time position is $141,000-$202,000 + 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

• Address testing research and engineering problems of GenAI with potential and for immediate real-world user impact for current and emergent query streams.
• Conduct applied research figuring out research developments best suited to solve open-ended problems.
• Break down problems to make iterative progress.
• Build end-to-end Machine Learning (ML) systems spanning data, modeling, evaluation and serving and search scale.
• Build a comprehensive understanding of the search stack, evaluation metrics, query sets and philosophy of improving search quality and presentation.