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.
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: $147000 - $210000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google .
Advance the platform to enable further Machine Learning (ML) use-cases for discover feed recommendations.
Design, implement, deploy, and maintain projects by collaborating with peer team members in ranking and retrieval to understand technical needs and system limitations.
Take end-to-end project ownership, encompassing ideation, implementation, analysis, and maintenance.
Learn and apply state-of-the-art techniques to transform the product and improve ML serving.
Utilize AI tools and technologies, and be prepared to discuss your AI fluency and knowledge during the hiring process.
Minimum qualifications:
Bachelor’s degree or equivalent practical experience.
2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
2 years of experience with distributed systems architecture, scalability, reliability and performance optimization.
1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
1 year of experience integrating generative AI tools or LLM interfaces into workflows.
1 year of experience developing, fine-tuning, or deploying Large Language Models (LLMs) in production environments.
Preferred qualifications:
Master's degree or PhD in Computer Science or related technical fields.
Experience in system design for large-scale distributed systems using programming language C++.
Experience in data analysis of high-throughput streaming data and integrating real-time insights into systems.
Research experience in Machine Learning, Artificial Intelligence, or a related technical field (e.g., campus lab experience, research internships, or co-authored papers).
Strong understanding of ML systems, genAI technology, and Recommender Systems.
High velocity to iterate, strong ownership to land projects end-to-end and comfortable collaborating and supporting multiple client teams.
Bachelor’s degree or equivalent practical experience.
2 years of experience with software development in one or more programming languages, or 1 year of experience with an advanced degree.
2 years of experience with distributed systems architecture, scalability, reliability and performance optimization.
1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
1 year of experience integrating generative AI tools or LLM interfaces into workflows.
1 year of experience developing, fine-tuning, or deploying Large Language Models (LLMs) in production environments.