ML Models Implementation & Performance Optimization, Intern (Serbia)

At Tenstorrent, we believe the future of computing must be open, which is why our interns don’t just watch from the sidelines - they help build the core of it. We provide a "code-to-career" pipeline where students collaborate with industry experts to solve high-stakes problems in RISC-V and AI hardware-software co-design. By joining us, you are taking an internship to democratize high-performance computers that are accessible to everyone. 

In this role, you will implement state of art ML models on Tenstorrent hardware using Python and C++, focusing on pushing both accuracy and inference speed. You will work hands-on with Tenstorrent’s open-source software stack (tt-metalium, tt-nn, tt-llk), taking models from framework to silicon and iterating on performance. You will own a well-defined engineering project under the guidance of a dedicated mentor, with direct impact on how real workloads run on our chips. We are looking for a minimum of 3 months for this role with the potential for extension to 6 months. 

This role is onsite, based in our Belgrade office.

 

Who You Are

• Enrolled in the final year of BSc or MSc studies in Computer Science, Computer Engineering, Software Engineering, Electronics, Math, or a related field.
• Solid coding skills in Python and C++, with a basic understanding of machine learning concepts and frameworks.
• You have a passion for programming, are eager to learn, and enjoy solving complex performance and optimization problems.
• You are collaborative, open to feedback, and excited to work closely with experienced engineers and a dedicated mentor.

 

What We Need

• Implement functional ML models on Tenstorrent hardware using Python and popular ML frameworks like PyTorch.
• Benchmark, analyze, and optimize the performance of the implemented model's inference using existing tools and coding in C++ and Python.
• Collaborate with experienced engineers to validate the accuracy of implemented models and iterate on improvements.
• Contribute to performance optimization efforts where success is measured by achieving both high accuracy and fast execution (inference) of ML models on Tenstorrent hardware.

 

What You Will Learn

• How to implement state-of-the-art ML models on Tenstorrent hardware using Python, C++, and popular ML frameworks like PyTorch.
• Techniques for benchmarking, analyzing, and optimizing the performance of ML model inference using existing tools and code in C++ and Python.
• How to use (and potentially debug and fix) Tenstorrent’s open-source software libraries, such as tt-metalium, tt-nn, and tt-llk.
• How to collaborate with experienced engineers, apply various problem-solving techniques, and drive a well-defined engineering project under the guidance of a dedicated mentor.

 

Hiring Timelines

This internship opportunity is available throughout our 3 terms with the following corresponding recruitment cycles:

• Winter Term: Mar–May work term, Nov–Jan recruit.
• Summer Term: Jul/Aug–Sep work term, Jan–Apr/May recruit.
• Fall Term: Oct–Dec work term, Apr–May recruit.

Please note these timelines are for reference only. Actual timelines may vary.