Job Description

Minimum qualifications

• Bachelor’s degree or equivalent practical experience.
• 5 years of experience with software development in one or more programming languages.
• 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 one or more of the following: Speech/audio (e.g., technology duplicating and responding to the human voice), reinforcement learning (e.g., sequential decision making), ML infrastructure, or specialization in another ML field.
• 3 years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).

Preferred qualifications

• Master's degree or PhD in Computer Science or related technical field.
• 5 years of experience with data structures and algorithms.
• 1 year of experience in a technical leadership role.
• Experience developing accessible technologies.

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.

The Localization and Language for Search AI Answers team is at the forefront of teaching our AI to truly understand the world. We don't just translate words; we build systems that grasp cultural nuance, localized intent, and deep linguistic context.

As a horizontal team, our work spans the entire AI stack. Our ultimate mission is to drive deep, native contextualization across all AI components, ensuring that every user—no matter their language or locale—receives Search AI Answers that are accurate, fluid, and culturally aware.

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.

As a horizontal team, our work spans the entire AI stack. Our ultimate mission is to drive deep, native contextualization across all AI components, ensuring that every user—no matter their language or locale—receives Search AI Answers that are accurate, fluid, and culturally aware.

As a horizontal team, our work spans the entire AI stack. Our ultimate mission is to drive deep, native contextualization across all AI components, ensuring that every user—no matter their language or locale—receives Search AI Answers that are accurate, fluid, and culturally aware.

As a horizontal team, our work spans the entire AI stack. Our ultimate mission is to drive deep, native contextualization across all AI components, ensuring that every user—no matter their language or locale—receives Search AI Answers that are accurate, fluid, and culturally aware.

Responsibilities

• Write and test product or system development code.
• Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
• 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.
• Design and implement solutions in one or more specialized ML areas, leverage ML infrastructure, and demonstrate expertise in a chosen field.