Fabric SOC Architect

tenstorrent

Santa Clara, California, United States · Santa Clara · United States

Posted Sep 11, 2025

Other
Remote
Architecture

Job description

Tenstorrent 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.

At Tenstorrent, we’re building cutting-edge hardware and software solutions that power AI, HPC, and general-purpose workloads. As a Performance Architect on our Platform Architecture team, you’ll work across ML software stacks, compilers, CPU design, cache coherency protocols, and interconnect fabrics to shape the future of high-performance systems. This role is all about bridging software execution and silicon design—making data-driven decisions that directly influence our SoC performance.

This role is remote, based out of The United States.

We welcome candidates at various experience levels for this role. During the interview process, candidates will be assessed for the appropriate level, and offers will align with that level, which may differ from the one in this posting.

Who You Are

Passionate about solving complex system-level performance problems.
Comfortable working across hardware and software boundaries.
Analytical and data-driven, with a talent for turning workloads into architectural insights.
Collaborative, thriving in cross-functional teams spanning compilers, CPU, and interconnect.
Excited to shape the future of AI/HPC platforms through performance architecture.

What We Need

BS/MS/PhD in EE, ECE, CE, or CS
Deep understanding of NoC topologies, routing algorithms, QoS, and traffic scheduling.
Expertise in cache coherency protocols (AMBA CHI/AXI) and modern memory/IO technologies (DDR, LPDDR, GDDR, PCIe, CCIX, CXL).
Proficiency in C/C++ programming, with experience in building efficient performance models.
Familiarity with ML/AI traffic patterns or formal verification of cache coherence protocols is a strong plus.

What You Will Learn

How real ML/AI traffic patterns influence SoC interconnect and cache design.
The art of balancing performance vs. complexity in coherence and memory hierarchies.
How performance models feed into CPU and accelerator microarchitecture decisions.
Best practices for correlating pre-silicon and post-silicon performance.
Cutting-edge approaches to integrating heterogeneous compute systems at scale.

Compensation for all engineers at Tenstorrent ranges from $100k - $500k 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.