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.
In this role as Software Engineer in the Accelerated Linear Algebra (XLA TPU team involves developing the accelerated linear algebra (XLA) tensor processing units (TPU) parallelizing compiler, which partitions, optimizes, and runs large-scale machine learning models across multiple TPUs accelerators for internal Google, Google DeepMind, and external customers. You will have expertise in C++ programming, efficient data structures and algorithms, and outstanding technical communication skills.
Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Responsibilities
- Write C++ code for the Tensor Processing Unit (TPU) compiler, in collaboration with Google's software development AIs.
- Carry out design reviews with peers and stakeholders to make appropriate engineering choices, write design documentation and present technical material clearly to the team.
- Improve the compiler, scaling machine learning models across hardware accelerators like TPUs, Graphics Processing Units (GPUs) inside Google and Cloud customers.
- Conduct static and runtime performance analysis of important large-scale production models.
- Design and implement performance optimizations and critical features, which increase the velocity of important production teams.
Qualifications
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 2 years of experience with software development in one or more programming languages.
- 2 years of experience with computer architecture, performance analysis, and performance modeling.
- 2 years of experience with performance, large-scale systems data analysis, visualization tools, or debugging.
- 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 or related technical fields.
- 2 years of experience with data structures and algorithms.
- Experience optimizing distributed programs at large-scale and experience with Compilers and Compiler construction.
- Experience in Machine Learning and high performance computing (HPC).
- Excellent debugging and programming concurrent/parallel computations, and working on accelerators including very long instruction word (VLIW) and Vector machines, GPUs, or digital signal processors (DSPs).