Tenstorrent

Software Engineer role focused on developing and optimizing an MLIR-based AI compiler (TT-Forge) to run AI models efficiently on Tenstorrent hardware. Involves optimizing computational graphs, creating custom dialects, and transformation passes, with a focus on training and multi-chip scaling.

Sr. Engineer, Software - AI Compiler role at Tenstorrent focused on developing TT-Forge, an MLIR-based compiler for Tenstorrent hardware, optimizing AI models for training and inference.

The role focuses on architecting and building scalable cycle-accurate AI accelerator performance models to inform hardware design and optimization. This involves defining abstraction layers, leading performance modeling, and integrating models into larger simulation environments.

The role involves architecting, integrating, and deploying AI/ML-driven solutions into production physical design flows for advanced semiconductor nodes. This includes creating custom CAD tools and optimizing EDA tools using data-driven and ML-based techniques to improve PPA and runtime. The engineer will also develop and enhance RTL-to-GDS methodologies.

Sr. Engineer, Kernel Development and Optimization at Tenstorrent, focusing on designing, implementing, and optimizing performance-critical kernels for AI hardware, including matrix multiplication and attention primitives. The role involves host-side orchestration, parallelization, developing benchmarks and tests, and collaborating with compiler, runtime, ML, and hardware teams to integrate kernels into production systems. Experience with C++, low-level software, concurrency, and data-driven optimization is required.

Software Engineer focused on developing and optimizing performance-critical kernels for AI hardware, targeting ML and HPC workloads. This role involves C++ systems engineering, low-level optimization, and close collaboration with hardware and software teams.

Software Engineer on the Metal Runtime team working on low-level software for AI accelerators, focusing on scheduling, memory movement, and efficient execution across parallel processors. The role involves building and optimizing high-performance runtime systems close to the hardware.

The role focuses on optimizing the energy efficiency of RISC-V based CPUs and AI Data Centers for Tenstorrent, a company at the forefront of AI technology. The Power Architect will be responsible for power management, SoC power architecture, power delivery networks, thermal analysis, and performance trade-offs, with a specific emphasis on analyzing AI and ML workloads for performance and efficiency. This is a hybrid role based in Santa Clara, CA, with opportunities for growth and impact in the AI hardware design space.

The role focuses on developing software for state-of-the-art AI inferencing on Tenstorrent's hardware, including designing APIs, deploying workloads, and benchmarking inference speed. It involves optimizing end-to-end ML inference on custom silicon and building scalable software interfaces.

Software Engineer role focused on developing and scaling an MLIR-based AI compiler (TT-Forge) for Tenstorrent, involving graph transformations, lowering passes, and kernel optimizations to support both training and inference on custom chip architectures.

Software Engineer role focused on developing and optimizing distributed software systems for AI and HPC clusters, specifically for distributed inference and training infrastructure. Requires strong C/C++ systems programming, distributed computing principles, and experience with MPI-based technologies.

Software Engineer role focused on building and optimizing TT-Fabric, a low-level networking library for Tenstorrent's AI compute clusters. The role involves architecting, implementing, and maintaining the networking layer that connects thousands of AI processors for distributed training and inference, optimizing protocols and data movement for maximum hardware performance.

Lead a team of Verification Engineers to validate the functionality and performance of next-generation AI hardware, focusing on AI-specific data types, compute patterns, and on-chip network validation.

Software Engineer role focused on developing and optimizing Tenstorrent's MLIR-based AI compiler (TT-Forge) to run AI models efficiently on Tenstorrent hardware. Responsibilities include optimizing computational graphs, creating custom dialects and transformation passes, and potentially developing human-in-the-loop tuning tools.