Staff ML Software Engineer, Search Ecosystem

In most instances, this position requires in-person interviews as part of the hiring process.

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development, including 5 years of experience testing and launching software products, and 3 years of experience with software design and architecture.
• 5 years of experience in building ML systems and infrastructure (e.g., for model training, evaluation, deployment, serving).
• Experience integrating generative AI tools or LLM interfaces into workflows.
• Experience in data processing software (e.g., Hadoop, Spark, Beam, Hive), strategies (e.g., ETL, ELT, CDC) or algorithms (e.g., MapReduce, Flume).

Preferred qualifications:

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• 3 years of experience in a technical leadership role leading project teams and setting technical direction.
• 3 years of experience working in a complex, matrixed organization involving cross-functional or cross-business projects.
• Experience in full-stack development, building back-end distributed infrastructure, and programming in Java, Python, or C++.
• Experience integrating generative AI tools and understanding of AI architecture.

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 Search Ecosystem team builds a sustainable content ecosystem for Google products. We develop scalable platforms to address Google's content requirements by systematically identifying knowledge gaps in our AI models, sourcing high-quality content to fill them, and then training the models to verify performance improvements. This is a high-impact, high-visibility growth area within Search Ecosystems.

This is an opportunity to work on a business-critical product that is redefining the boundaries of artificial intelligence. We are a fast-moving, growing organization working on highly impactful projects. In this role, you will help build robust systems and scalable platforms that power content acquisition to train AI models in a responsible and secure manner. 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: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

• Develop and implement frameworks and methodologies for evaluating the quality and effectiveness of AI system components and data.
• Analyze evaluation results to drive improvements in system performance and underlying value models.
• Play a key role in building and articulating the high-level technical strategy for your area of ownership.
• Leverage knowledge of LLMs to design, build, and maintain specialized infrastructure on top of LLMs
• Drive technical projects from ideation through to production, working effectively in an ambiguous and fast-paced environment.