Tenstorrent
Tenstorrent is seeking an Engineer for CPU Performance Verification to drive performance verification and correlation between RTL and performance models for a high-performance RISC-V CPU. The role involves collaborating with core architects and RTL teams to debug miscorrelation, verify performance features, and optimize power, performance, and area. Candidates will gain end-to-end exposure to CPU design, from microarchitecture through performance convergence and validation, in a highly technical and collaborative environment.
Tenstorrent is a next‑generation computing company that builds computers optimized for AI workloads. They design AI silicon and systems—leveraging RISC‑V, custom ASICs, and advanced architecture—alongside neural‑network compilers and software to accelerate training and inference. Headquartered in the U.S. with offices in Austin and Silicon Valley and global teams in Toronto, Belgrade, Seoul, Tokyo, and Bangalore, Tenstorrent brings together expertise in computer architecture, ASIC design, and compiler technology. Their hardware‑plus‑software approach targets developers, researchers, and enterprises needing scalable, efficient AI compute.
Tenstorrent Unveils TT-QuietBox(TM) 2, the First RISC-V AI Workstation With a Fully Open-Source Stack to Deliver Teraflop-Class Inference
19 hours ago
Tenstorrent TT-QuietBox 2 Launched: A RISC-V Powered AI Workstation With 128 GB GDDR6 Memory, Liquid-Cooling & $9999 Starting Price
1 day ago
Layoffs At Tenstorrent As Startup Pivots Towards Developer Sales
Dec 9, 2025
Headquarters
San Francisco, CA
Company Size
201-500 employees
Founded
2018
Industry
Technology
Glassdoor Rating
4.2 / 5
Sarah Johnson
Chief Executive Officer
Michael Chen
Chief Technology Officer
Emily Williams
VP of Engineering
David Rodriguez
VP of Product
Jessica Thompson
Chief Financial Officer
Andrew Park
VP of Sales
View leadership team, funding history,
and employee contacts for Tenstorrent.
Tenstorrent Unveils TT-QuietBox(TM) 2, the First RISC-V AI Workstation With a Fully Open-Source Stack to Deliver Teraflop-Class Inference
1 day ago
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