Software Engineer II, Machine Learning Deployment, Silicon

Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 1 years of experience with software development in C++ and Python.
• 1 year of experience in data structures, algorithms, and software design.

Preferred qualifications:

• Master's degree or PhD in Computer Science or related technical field.
  
• Experience with machine learning frameworks (e.g., TensorFlow, PyTorch).
• Experience with performance analysis, profiling tools, or debugging.
• Knowledge of computer architecture concepts.
• Excellent communication and collaboration skills.

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.

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

• Support the deployment and optimization of ML models from third-party developers onto Google Tensor SoCs.
• Profile ML model execution on-device to identify performance bottlenecks across CPU, GPU, TPU, and memory subsystems.
• Investigate performance issues related to Tensor SoC architecture, ML frameworks, and kernel optimization.
• Implement solutions, such as code modifications or utilizing optimized libraries.
• Ensure compilation of ML models by working with partner teams and utilizing relevant tools.