Working Student - LLM Inference Performance

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.

* 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 ha

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