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

• 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 data movement to extract maximum hardware performance.
• Integrate TT-Fabric APIs into the broader programming model in collaboration with AI and hardware teams.
• Help define the long-term architecture of Tenstorrent’s distributed systems stack.

What You Will Learn

• 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 infrastructure.

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.

As our TT-Distributed Software Engineer, you will develop and optimize distributed software systems that power the most efficient and highest-performing AI and HPC clusters. In this role, you'll work on distributed programming across multiple nodes, utilizing systems programming, inter-node communication, and Tenstorrent’s scalable architectures to advance the state-of-the-art distributed inference and training infrastructure.

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 You Are

• Strong C or C++ engineer with solid foundations in systems programming, operating systems, and distributed systems principles.
• Enthusiastic about distributed computing, including IPC, socket programming, and cluster resource coordination.
• Comfortable reasoning about scalability, fault tolerance, and performance across multi-node environments.
• Curious and first-principles thinker who challenges conventional approaches to distributed system design.
• Motivated to grow into a deep technical expert in large-scale distributed AI infrastructure.

What We Need

• Architect, implement, and optimize distributed software systems that coordinate computation and communication across clusters of AI accelerators and CPUs.
• Design and build distributed APIs enabling data-parallel and tensor-parallel AI workloads.
• Leverage MPI-based technologies and related frameworks to scale programming models across multiple hosts and compute nodes.
• Develop robust systems using IPC, inter-node sockets, and distributed communication primitives to ensure reliability and high performance.
• Build and maintain testing, debugging, profiling, and monitoring tools for large-scale distributed workloads and collaborate with model and systems teams on cluster bring-up.

What You Will Learn

• How large-scale distributed inference and training systems are architected across thousands of accelerators.
• Advanced techniques in collective communication, synchronization, and parallel workload distribution.
• The performance characteristics of Tenstorrent hardware in multi-node cluster environments.
• How distributed programming models integrate with compilers, runtimes, and AI frameworks.
• The real-world challenges of deploying, debugging, and scaling next-generation AI clusters.