Low-Level Systems Engineer - AI Hardware & Networking

Tenstorrent; Santa Clara (CA); Hybrid; USD 100,000 - 500,000; Full time

Job summary

Tenstorrent is seeking a strong systems engineer with deep C/C++ experience for a hybrid role based in Santa Clara, CA; Austin, TX; or Toronto, ON. The candidate will design, implement, and maintain TT-Fabric, a low-level networking library powering distributed AI inference and training.

Ideal applicants are passionate about hardware-software interaction, performance tuning, and scalable distributed systems. Compensation ranges from $100k to $500k with location-based variation.

Qualifications

• 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.

Responsibilities

• 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.

Skills

C/C++

Networking

Bare-metal

Tools

TT-Fabric

Job description

Tenstorrent is seeking a strong systems engineer with deep C/C++ experience for a hybrid role based in Santa Clara, CA; Austin, TX; or Toronto, ON. The candidate will design, implement, and maintain TT-Fabric, a low-level networking library powering distributed AI inference and training.

Ideal applicants are passionate about hardware-software interaction, performance tuning, and scalable distributed systems. Compensation ranges from $100k to $500k with location-based variation.