Data Movement Architecture Intern
Tenstorrent University Jobs
Apply to this jobTenstorrent is leading the industry on cutting-edge AI technology, revolutionizing performance expectations, ease of use, and cost efficiency. With AI redefining the computing paradigm, solutions must evolve to unify innovations in software models, compilers, platforms, networking, and semiconductors. Our diverse team of technologists have developed a high performance RISC-V CPU from scratch, and share a passion for AI and a deep desire to build the best AI platform possible. We value collaboration, curiosity, and a commitment to solving hard problems. We are growing our team and looking for contributors of all seniorities.
Our team has built a high-performance RISC-V CPU from the ground up and is committed to building the best AI platform possible. We value collaboration, curiosity, and a passion for solving hard technical problems across software, compute, networking, and semiconductors.
As part of the Platform Architecture team, the Data Movement Architecture Intern will help shape the performance of our next-generation AI, CPU, and heterogeneous platforms. You’ll work closely with architects and software engineers to design, model, and analyze how data movement subsystems impact system behavior and application-level performance.
This role is remote, based out of Austin, TX, Santa Clara, CA or Fort Collins, CO.
Who You Are
- A highly motivated student who enjoys deep technical exploration and performance analysis.
- Curious about how data moves across compute, memory, and networking in complex AI/CPU systems.
- Comfortable interpreting simulation data and evaluating architecture tradeoffs.
- A strong communicator who collaborates well across multidisciplinary teams.
What We Need
- Pursuing an MS/PhD in Computer Engineering, Computer Science, Electrical Engineering, or a related field.
- Solid understanding of computer architecture topics such as interconnects, memory hierarchies, and caching.
- Proficiency in C/C++ and scripting languages like Python or Shell.
- Experience with RTL design and simulation.
What You Will Learn
- Designing and optimizing data movement subsystems including CPU controllers, DMA engines, and accelerators.
- Conducting performance exploration for CPU controller features and system-level data movement.
- Performing PPA analysis and developing reduced data movement kernels for modeling and emulation.
- Working with software, RTL, and architecture teams to understand real-world AI/CPU workloads.
Compensation for all interns at Tenstorrent ranges from $50/hr - $70/hr including base and variable compensation targets. Experience, skills, education, background and location all impact the actual offer made.
Tenstorrent offers a highly competitive compensation package and benefits, and we are an equal opportunity employer.This offer of employment is contingent upon the applicant being eligible to access U.S. export-controlled technology. Due to U.S. export laws, including those codified in the U.S. Export Administration Regulations (EAR), the Company is required to ensure compliance with these laws when transferring technology to nationals of certain countries (such as EAR Country Groups D:1, E1, and E2). These requirements apply to persons located in the U.S. and all countries outside the U.S. As the position offered will have direct and/or indirect access to information, systems, or technologies subject to these laws, the offer may be contingent upon your citizenship/permanent residency status or ability to obtain prior license approval from the U.S. Commerce Department or applicable federal agency. If employment is not possible due to U.S. export laws, any offer of employment will be rescinded.
Summary
Assist in designing and analyzing data movement subsystems for AI/CPU platforms.
Job title
Data Movement Architecture Intern
Experience level
student pursuing MS/PhD
Industry
software
Location requirements
remote, based in Austin, Santa Clara, or Fort Collins; remote allowed
Salary
50/hr - 70/hr
Management role
No
Required skills
Preferred skills
Specializations
Structured locations inferred from the posting.
Austin, TX, USA
Fort Collins, CO, USA
Santa Clara, CA, USA