The Tenstorrent team combines technologists from different disciplines who come together with a shared passion for AI and a deep desire to build great products. We value collaboration, curiosity, and a commitment to solving hard problems. Find out more about our culture.

Responsibilities

Develop machine learning graph compiler
Participate in the co-design of Tenstorrent’s hardware and software stack
Benchmark, analyze, and optimize performance of key machine learning applications across Tenstorrent’s hardware and software stack
Develop performance analysis and estimation infrastructure that feeds into Tenstorrent compiler
Develop high-performance run-time engine
Integrate the Tenstorrent software into leading machine learning frameworks
Work closely with machine learning engineers to discover the hardware and software requirements of current and future machine learning applications

Experience & Qualifications

BSc, MSc or PhD in Electrical/Computer Engineering or Computer Science
Experience with algorithms, data structures, and software development in C/C++. Python expertise is welcome as well
Familiarity with and passion for any of the following -- machine learning, compilers, parallel programming, high-performance and massively parallel systems, processor and computer architecture -- is a plus

Locations:

Austin, TX | Santa Clara, CA | Toronto, ON

Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer.

Due to U.S. Export Control laws and regulations, Tenstorrent is required to ensure compliance with licensing regulations when transferring technology to nationals of certain countries that have been sanctioned by the U.S. government.

As this position will have direct and/or indirect access to information, systems, or technologies that are subject to U.S. Export Control laws and regulations, please note that citizenship/permanent residency information and/or documentation will be required and considered as Tenstorrent moves through the employment process.