Google

Minimum qualifications:

• Bachelor's degree in Computer Science, Electrical Engineering, related technical field or equivalent practical experience.
• 4 years of relevant work experience, including development, testing and deployment of embedded systems.
• Experience in C/C++ or Python programming and Machine Learning.

Preferred qualifications:

• Master's degree in signal processing, data science, and machine learning.
• 3 years of experience with AI/ML algorithms and tools (e.g., TensorFlow), deep learning, or natural language processing.
• Experience in audio, speech, machine learning, DSP and embedded systems.
• Excellent analytical and problem-solving skills and excellent leadership, organization, communication, and teamwork skills.

About the Job

Our computational challenges are so big, complex and unique we can't just purchase off-the-shelf hardware, we've got to make it ourselves. Your team designs and builds the hardware, software and networking technologies that power all of Google's services. As a Hardware Engineer, you design and build the systems that are the heart of the world's largest and most powerful computing infrastructure. You develop from the lowest levels of circuit design to large system design and see those systems all the way through to high volume manufacturing. Your work has the potential to shape the machinery that goes into our cutting-edge data centers affecting millions of Google users.

Google's mission is to organize the world's information and make it universally accessible and useful. Our Devices & Services team combines the best of Google AI, Software, and Hardware to create radically helpful experiences for users. We research, design, and develop new technologies and hardware to make our user's interaction with computing faster, seamless, and more powerful. Whether finding new ways to capture and sense the world around us, advancing form factors, or improving interaction methods, the Devices & Services team is making people's lives better through technology.

Responsibilities

• Work on Machine Learning (ML) projects on speech and audio applications, leveraging techniques and approaches, using Generative AI and Large Language Models (LLM).
• Engage in memory and performance optimization to ensure the deployment of ML models on Google hardware.
• Collaborate with Product, Research, and Software teams to comprehend ML requirements and deliver solutions that meet key user experiences.
• Work alongside tools and architecture teams to streamline the deployment, validation, and testing processes for Google hardware.
• Develop ML models for Google hardware devices.