Working Student - LLM Inference Performance

About Tensordyne AI is transforming our world. It can perform cognitive functions that previously only humans could do, such as perceiving interactions across different modalities and environments - with the ability to quickly learn and then solve complex problems. Tensordyne is an AI system solution company that builds very high-performance, low-power generative AI inference systems. Our mission, through the creation of custom silicon, hardware and software, is to enable multimodal Generative AI inference acceleration at scale, with safe, sustainable, high-performance systems for our hyperscaler and neocloud data center customers. We are at the leading edge of advancing the latest research and product improvements for generative Al inference solutions that will make Al even more advantageous for compelling new generative AI applications.Tensordyne is a well funded, fast-paced startup company with headquarters in both Sunnyvale, CA, and Munich, Germany. We also have many talented team members working remotely across North America and Europe. We take care of our people and their families with comprehensive benefits, competitive compensation, flexible spending options, and recognition programs, because building category-defining technology starts with a healthy, supported team. Come join us as we shape the future of multimodal generative artificial intelligence! About the Role How fast should an LLM building block run on our hardware? How fast does it actually run, and what would make it faster? As a Working Student, LLM Inference Performance, you'll help answer these questions by working directly with our hardware and software engineering teams. You'll write and optimize low-level kernels, benchmark performance on real silicon, and develop profiling tools that help engineers understand and improve LLM inference performance. You'll gain hands-on experience with our Rust programming stack and Python SDK, use AI coding agents as part of your development workflow, and collaborate closely with engineers working on compilers, runtimes, and kernel optimization. This is an opportunity to apply your academic knowledge to real-world AI hardware challenges while taking ownership of meaningful technical projects. What You'll Do • Understand our hardware: Learn how Tensordyne silicon processes data, manages memory, and synchronizes operations, and how key LLM components such as attention, MLP, and Mixture-of-Experts (MoE) routing map onto the architecture.. • Develop and optimize kernels: Implement, test, and tune low-level kernels to improve the performance of LLM inference workloads.. • Analyze performance: Build analytical and roofline performance models, compare predictions against simulator and real-silicon measurements, and investigate performance bottlenecks.. • Build benchmarking and profiling tools: Develop benchmark harnesses, profiling scripts, and trace visualizations that make performance easier to measure, understand, and improve.. • Work with AI coding agents: Explore workflows where AI agents assist with kernel development, optimization, and benchmarking, and identify the tools and context they need to work effectively.. • Collaborate across engineering teams: Work closely with compiler, runtime, and kernel engineers to translate performance insights into practical improvements.. Example project: Develop a benchmarking and profiling harness for an LLM building block, such as decode attention, at two abstraction levels. Compare measured performance against theoretical estimates and make the tooling accessible to AI coding agents. What We're Looking For • Currently enrolled in a Master's degree program in Computer Science, Electrical Engineering, Mathematics, or a related technical field.. • Strong programming sk