Software Engineer, TT-Fabric

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 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 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 infrastructure that directly impacts large-scale AI training and inference performance.

What We Need