Tenstorrent
Technology
AccelerationKernelDeveloperIntern
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optimal for Mid+ candidates.
“Acceleration Kernel Developer Intern at Tenstorrent”
Industry & Context.
Full Job Description
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. At Tenstorrent, we believe the future of computing must be open, which is why our interns help build the core of it. You will work on real performance problems across kernels, tensor compute, data movement, and machine learning software. As an Acceleration Kernel Developer Intern at Tenstorrent, you will help optimize low-level workloads and develop kernels for machine learning applications. You will work closely with engineers across software and machine learning to improve performance, reliability, and efficiency across the stack. This role is a 6-month full-time, on-site internship based in our Gdańsk, Poland office. Who You Are A student pursuing Computer Science, Software Engineering, Computer Engineering, Engineering Science, or a related field (math, physics) with software development experience. . Strong in low-level algorithm implementation and optimization, with experience in parallel acceleration on CPUs or GPUs. Proficient in C/C++ and Python, with familiarity with machine learning frameworks and profiling or debugging tools. Self-driven, detail-oriented, collaborative, and comfortable solving complex technical problems. What We Need Develop and optimize kernels and kernel libraries for machine learning and HPC workloads. Improve tensor compute and data movement performance through low-level optimization. Profile bottlenecks, debug is
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