Software Engineer, Machine Learning Compilers, Silicon Full-time

Minimum qualifications:

• Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience.
• 5 years of experience with software development in C , and with data structures/algorithms.
• 3 years of experience testing, maintaining, or launching software products, with 1 year of experience with software design and architecture.

Preferred qualifications:

• Master’s degree or PhD in Computer Science, a related technical field, or equivalent practical experience.
• Experience in power and performance optimizations.
• Experience with domain-specific compilers for machine learning.
• Knowledge of hardware that provides a degree of parallelism.

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.

As a Software Engineer, you will work on developing Machine Learning (ML) compilers for the Tensor TPU to accelerate machine learning models running on custom hardware accelerators. In this role, you will manage project priorities, deadlines, and deliverables.

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

• Build compilers and tools that map Machine Learning models to the hardware Information Security Assurance.
• Evaluate various trade-offs of different parallelization strategies such as performance, power, energy, and memory consumption.
• Collaborate with machine learning researchers to improve the domain-specific compiler.
• Collaborate with hardware engineers to evolve future accelerators.