AI Application Engineer

Be part of a team that pushes boundaries, developing custom silicon solutions that power the future of Google's direct-to-consumer products. You'll contribute to the innovation behind products loved by millions worldwide. Your expertise will shape the next generation of hardware experiences, delivering unparalleled performance, efficiency, and integration.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.

Responsibilities

• 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 in Electrical Engineering, Computer Engineering, Computer Science, a related field, or equivalent practical experience.
• 8 years of experience with software development and engineering.
• Experience coding in one or more general-purpose programming languages (e.g., Python and C++).
• Experience with Machine Learning frameworks (e.g., TensorFlow, PyTorch, or JAX).
• Experience implementing, deploying, and maintaining machine learning models and pipelines in production environments.

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.