Senior Software Engineer, Generative AI, Search Personalization

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience with software development in C++ and Python.
• 3 years of experience testing, maintaining, or launching software products, and 1 year of experience with software design and architecture.
• 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
  
• 3 years of experience implementing and evaluating AI algorithms in practical applications.
• 1 year of experience with GenAI techniques (e.g.,
  Large Language Models (LLMs), multi-modal, large vision models) or with GenAI-related concepts (e.g., language modeling, computer vision).

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical fields.
• 3 years of experience working in a complex organization involving cross-functional, or cross-business projects.
• 1 year of experience in a technical leadership role.
• Experience working on a search infrastructure or search quality platform.
• Experience developing accessible technologies.
• Ability to comfort with ambiguity and excitement for thinking.

Ability to comfort with ambiguity and excitement for thinking.Ability to comfort with ambiguity and excitement for thinking.

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.

Search Personalization team drives improvements to search through better understanding of the user interests and preferences and unlocks new search experiences that leverage rich user inputs and context (e.g., multimodal inputs, conversations).

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.

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

• Develop auto evaluation models and deeply integrate them into the development stack to improve the model reasoning capabilities when personalization context is present.
• Interface with product managers and other teams/engineers to conceive and implement solutions that measure and validate product directions and then scale to production requirements.
• Train and debug machine learning models specifically targeted at evaluating personalized search results and multi-turn search sessions.
• Analyze experimental results and help decide the launchable state for projects.
• Bootstrap quality for new products with little user data.