Software Engineer, Machine Learning, Edge TPU Full-time

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
• 2 years of experience with software development in one or more programming languages (e.g., C , Python), and with data structures or algorithms.
• 2 years of experience with machine learning algorithms and tools (e.g. TensorFlow), artificial intelligence, deep learning, or natural language processing.
• Experience with Large Language Models, NLP, or Generative AI.

Preferred qualifications:

• Master’s degree or PhD in Computer Science or related technical field with an emphasis on Machine Learning/Artificial Intelligence.
• Experience in optimizing ML models for inference.
• Experience in developing and training machine learning models.
• Experience working with any deep learning frameworks (e.g., Tensorflow, Pytorch, or JAX).
• Publications or research experience in deep learning.

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.

You will work as part of the EdgeTPU Applied Machine Learning team. We are leading the efforts of defining, developing and training edge optimized models for Generative AI, computer vision, natural language, and speech use cases. You will design, build, and maintain model optimization tools and infrastructure modules needed for automating optimization and training of neural networks and architecture design space exploration. Additionally, you will closely collaborate with ML model developers, researchers, and EdgeTPU hardware/software teams to accelerate the transition from research ideas to user experiences running on the EdgeTPU.

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.

The US base salary range for this full-time position is $136,000-$200,000 bonus equity benefits. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.

Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google .

Responsibilities

• Design, build, and maintain model optimization tools and infrastructure modules needed for automating optimization and training of neural networks and architecture design space exploration.
• Provide cross-team support for compiler, architecture exploration, and neural network design.
• Write modular and efficient machine learning training pipelines and assist in building profiling and visualization tools.
• Use existing and newly developed tools to drive ML optimization infrastructure.
• Work with EdgeTPU architects to design future accelerators, the hardware/software interface, and co-optimizations of the next generation EdgeTPU architectures.