Job Type
Job Details
- Bachelor’s degree or equivalent practical experience.
- 5 years of experience with software development in one or more programming languages, and with data structures/algorithms.
- 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 machine learning algorithms and tools (e.g. TensorFlow), artificial intelligence, deep learning or natural language processing.
- Experience with Large Language Models, NLP, or Generative AI.
Preferred qualifications:
- Master's degree or PhD in Computer Science or related technical field.
- 1 year of experience in a technical leadership role.
- Experience developing accessible technologies.
- Experience in applied Machine Learning including data collection, analysis, and feature engineering.
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.
To co-design solutions while applying them to critical Google problems, we will start with creating decision experts for Search Quality that can replace or assist human domain experts in the areas of ranking and analyzing (given the feedback from raters and users, how to make the decision which ranking is better). We will apply these decision experts to transformatively improve service in production core ranking models, create more advanced models, and attempt to formulate Search Engine Results Page (SERP) generation as a generative AI task.
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 $161,000-$239,000 + 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.
- Work closely with other Search Quality engineers by giving feedback and guiding the development of necessary tooling and infrastructure that supports your work.
- Work in context of real applications to important Google products, and in partnership with product teams and other research engineers (in particular Search Generative Experience, Bard, and Gemini teams).
- Work with multiple code bases and machine learning libraries.
- Work on developing a wide range of solutions, both modeling and infrastructure related (e.g., from how to store the new knowledge, how to efficiently use LLMs to evaluate thousands of requests, how to improve those models and make them smarter).
- Triage product or system issues and debug/track/resolve by analyzing the sources of issues and the impact on hardware, network, or service operations and quality.
