Tenstorrentuniversity

Tenstorrentuniversity

Machine Learning for Physical Design Intern - CPU/AI Hardware

Austin, Texas, United States · Intern · Internship

Sponsorship not specifiedDetected 8 days ago
PythonData StructuresAlgorithmsMachine LearningStatisticsCollaboration

About the role

  • With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors.
  • We value collaboration, curiosity, and a commitment to solving hard problems.
  • The work is done collaboratively with a group of highly experienced engineers across various domains of the ASIC.

Requirements

  • Possessing deep knowledge of math, probability, statistics, and algorithms.
  • Practical experience in extending existing ML systems and implementing cutting-edge algorithms.
  • Experience collaborating with a group of highly experienced engineers across various domains of the ASIC implementation.
  • 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 licensing conditions set by the U.S. government.
  • If a U.S. export license is required, employment will not begin until a license with acceptable conditions is granted by the U.S. government.

Compensation

  • for all interns at Tenstorrent ranges from $50/hr - $70/hr including base and variable compensation targets.

Benefits

  • Experience, skills, education, background and location all impact the actual offer made.

Equal opportunity

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

Visa & Work Authorization

  • 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 licensi

This listing is sourced directly from Tenstorrentuniversity's careers page and normalized into a canonical job model.