Software Engineer, TPU Compiler, PhD, Early Careers
Indexed description
- Experience with coding in data structures, algorithms and software design.
- Research experience in Artificial Intelligence, Distributed Systems, Machine Learning, Data Mining, Natural Language Processing, Image Classification, Spam Fighting, or related fields.
- Work or educational experience in Machine Learning or Artificial Intelligence.
- Currently enrolled in or graduated from a PhD program.
- Experience working with parallel computing.
- Experience with compilers and compiler construction.
- Excellent debugging and programming concurrent/parallel computations, and working on accelerators such as VLIW, Vector machines, GPUs, or DSPs.
TPU team develops the Accelerated Linear Algebra (XLA) TPU parallelizing compiler used to partition, optimize, and run large-scale machine learning models across multiple TPU accelerators for internal (e.g. Google DeepMind) and external customers. It is a vital part of the Google Gemini software infrastructure.
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 product or system development code for the TPU compiler (in C++).
- Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
- Contribute to a compiler which scales-out machine learning models across accelerators like TPU/Graphics Processing Unit (GPU) at Google and Cloud.
- Design and implement performance optimizations and critical features, which increase the velocity of important production teams.
- Apply AI to the development of the Compiler and to the Compiler itself.
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