Software Engineer, TT-Fabric · 💰 $100k - $500k
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August 5, 2026 at 10:41 PM
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.
Tenstorrent is building the world’s fastest, most efficient AI compute clusters. TT-Fabric is the high-performance nervous system of this platform: the low-level networking layer that lets thousands of RISC-V and AI processors snap together into a single, massively parallel distributed supercomputer. If you love squeezing nanoseconds out of hot paths, designing protocols that move class="bg-amber-100">data at absurd scale, and turning messy hardware constraints into elegant distributed systems, this is an opportunity to shape the fabric that future AI models will run on
This role is hybrid based out of Santa Clara, CA; Austin, TX; or Toronto, ON.
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 We Are
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.
Tenstorrent is building the world’s fastest, most efficient AI compute clusters. TT-Fabric is the high-performance nervous system of this platform: the low-level networking layer that lets thousands of RISC-V and AI processors snap together into a single, massively parallel distributed supercomputer. If you love squeezing nanoseconds out of hot paths, designing protocols that move class="bg-amber-100">data at absurd scale, and turning messy hardware constraints into elegant distributed systems, this is an opportunity to shape the fabric that future AI models will run on
This role is hybrid based out of Santa Clara, CA; Austin, TX; or Toronto, ON.
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 We Are
- Strong systems engineer with deep C or class="bg-amber-100">C++ experience and comfort working in low-level or bare-metal environments.
- Passionate about hardware-software interaction, performance tuning, and eliminating inefficiencies at the protocol level.
- Curious about networking, synchronization, and communication across large clusters.
- Comfortable reasoning from first principles and challenging industry conventions.
- Motivated by building class="bg-amber-100">infrastructure that directly impacts large-scale AI training and inference performance.
- Architect, implement, and maintain TT-Fabric, our low-level networking library powering distributed inference and training.
- Design scalable communication systems capable of coordinating thousands of AI processors efficiently and reliably.
- Optimize protocols, synchronization strategies, and class="bg-amber-100">data movement to extract maximum hardware performance.
- Integrate TT-Fabric APIs into the broader class="bg-amber-100">programming model in collaboration with AI and hardware teams.
- Help define the long-term architecture of Tenstorrent’s distributed systems stack.
- How large-scale AI clusters are architected from the networking layer up.
- The performance characteristics of custom AI hardware and RISC-V processors at scale.
- Advanced synchronization, collective communication, and interconnect optimization techniques.
- How distributed systems design decisions directly influence model throughput and training efficiency.
- How hardware and networking software co-evolve in next-generation AI class="bg-amber-100">infrastructure.
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.