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.
Within Labs, the Language Applications team manages a high-impact portfolio, including AI Studio, the Gemini API, and NotebookLM. The role is within GemFM, the team responsible for multimodal artifact generations.
Our focus is on synthetic audio, video, code, and any multimodal generation. We are the team that built audio overviews and cinematic video overviews, and we are seeking passionate ML SWEs to help us expand the boundaries of creative content generation, redefining how and where these new forms of media are made.
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: $174000 - $253000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities
- Write and test product or system development code. Write, test, and maintain production-ready code for NotebookLM’s Content Studio, focusing on multimodal media pipelines (synthetic audio, video, and code).
- Collaborate with peers and stakeholders through design and code reviews. Partner with engineering, UX, and research teams to ensure best practices in prompt engineering, model safety, and system efficiency.
- Contribute to documentation and educational content. Create and update technical documentation and guidelines, adapting content to keep pace with model upgrades and user feedback.
- Diagnose and resolve pipeline anomalies within the GemFM generator, optimizing for TPU/GPU performance, network latency, and output quality.
- Design and deploy advanced solutions in specialized ML fields (such as voice synthesis or long-context LLMs) using internal ML infrastructure.
Qualifications
Minimum qualifications:
- Bachelor’s degree in Computer Science, Mathematics, or equivalent practical experience.
- 5 years of experience with software development in programming languages like Python, C++, or Java.
- 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 ML infrastructure (including model deployment, evaluation, optimization, and debugging).
- 3 years of experience with speech/audio (e.g., voice synthesis), Generative AI/multimodal media synthesis, reinforcement learning, or another ML field.
Preferred qualifications:
- Master's degree or PhD in Computer Science, Machine Learning, or related technical field.
- 5 years of experience with advanced data structures, algorithms, and machine learning optimization.
- 1 year of experience in a technical leadership role, guiding rapid prototyping or directing product-driven engineering efforts.
- Experience developing highly accessible consumer-facing technologies.
- Experience with advanced prompt engineering, creative ML applications, and building consumer products that utilize Large Language Models (LLMs) or multimodal media formats (such as video, audio, or image synthesis).