Software Engineer, AI Application Engineering, Silicon

Design and develop robust toolchains to support and accelerate multimedia AI activities.Develop specialized AI agents and orchestration architectures to work together seamlessly on complex goals.Implement, deploy, and scale multimedia Machine Learning (ML) pipelines across Google Cloud and edge devices.Optimize machine learning models for improved performance, latency, and deployment efficiency.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.1 year 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.1 year 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, Artificial Intelligence, Machine Learning, or a related field.Experience with multimedia applications, video processing, or computer vision ML tasks.Experience building and scaling ML pipelines on cloud infrastructure (e.g., Google Cloud Platform) and deploying optimized models to edge devices (e.g., mobile, embedded systems).Experience with model optimization techniques for performance and latency reduction (e.g., quantization, pruning, hardware-aware tuning).Familiarity with AI agent architectures, large language models (LLMs), or orchestration systems (e.g., LangChain, AutoGen).Strong understanding of distributed systems, system architecture, and toolchain development for engineering teams.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.Our AI engineering mission is to accelerate multimedia innovation from concept to silicon. We achieve this by building reliable toolchains, rapidly prototyping ML pipelines, and developing agentic multimedia verification workflows that validate our solutions for next-generation chips.Google's mission is to organize the world's information and make it universally accessible and useful. Our team combines the best of Google AI, Software, and Hardware to create radically helpful experiences. We research, design, and develop new technologies and hardware to make computing faster, seamless, and more powerful. We aim to make people's lives better through technology.Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $147000 - $210000 (USD) + 15% bonus target + equity + benefitsLearn more about benefits at Google.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.1 year 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.1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).