Job Description

Minimum qualifications

• Bachelor’s degree in Computer Science, Mathematics, or equivalent practical experience.
• 8 years of experience in software development.
• 5 years of experience testing and launching software products, and 3 years of experience with software design and architecture.
• 5 years of experience with speech/audio, reinforcement learning, ML infrastructure, or another specialized ML field.
• 5 years of experience leading ML design, optimizing end-to-end ML (deployment, evaluation, tuning), and integrating generative AI tools or Large Language Model (LLM) interfaces.

Preferred qualifications

• Master’s degree or PhD in Engineering, Computer Science, or a related technical field.
• 8 years of experience with advanced data structures and algorithms.
• 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 building distributed queueing infrastructure or rapidly prototyping cross-stack generative AI applications.

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.

At Google Labs, we incubate early-stage technologies that define the future of computing and AI. We operate as small, autonomous, product-driven teams that move with startup speed, take bold technical bets, and build breakthrough user experiences. Within Labs, the Language Applications organization builds foundational platforms and consumer products, including NotebookLM, Google AI Studio, and the Gemini API.

Our focus is pushing the frontier of synthetic media—audio, video, dynamic code, and interactive visual artifacts. We build the foundational systems that turn generative models into instant, intuitive, and reliable experiences for millions of creators, researchers, and knowledge workers worldwide.

Labs is a group focused on incubating early-stage efforts in support of Google’s mission to organize the world’s information and make it universally accessible and useful. Our team exists to help discover and create new ways to advance our core products through exploration and the application of new technologies. We work to build new solutions that have the potential to transform how users interact with Google. Our goal is to drive innovation by developing new Google products and capabilities that deliver significant impact over longer timeframes.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.

Responsibilities

• Design, develop, test, deploy, maintain, and enhance large-scale software solutions and resilient distributed backend infrastructure.
• Provide technical leadership on generative media projects, managing project priorities, deadlines, and architectural deliverables.
• Facilitate alignment and clarity across teams on goals, outcomes, and timelines, while influencing and coaching a distributed team of engineers.
• Lead the design and implementation of solutions in specialized ML areas, optimize ML infrastructure, and guide model optimization and data processing strategies.
• Prototype innovative cross-stack features and clean APIs that bridge core multimodal generation pipelines with interactive user interfaces.