About Tensordyne
Tensordyne is an AI system solution company that builds very high-performance, low-power generative AI inference systems. Through the creation of custom silicon, hardware and software, the company enables multimodal Generative AI inference acceleration at scale, with safe, sustainable, high-performance systems for hyperscaler and neocloud data center customers. Tensordyne is a well-funded, fast-paced startup with headquarters in Sunnyvale, CA, and Munich, Germany, and team members working remotely across North America and Europe.
Role
As a Working Student, LLM Inference Performance, you'll help answer how fast an LLM building block should run on Tensordyne hardware, how fast it actually runs, and what would make it faster. You'll work directly with the hardware and software engineering teams, 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 the 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.
Responsibilities
•Understand the 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.
Requirements
•Currently enrolled in a Master's degree program in Computer Science, Electrical Engineering, Mathematics, or a related technical field.
•Strong programming skills in Python and at least one of C, C++, or Rust.
•Understanding of computer architecture, memory systems, bandwidth, and parallel computing through academic coursework or personal projects.
•Interest in low-level programming, hardware performance, and optimization.
•Practical experience using AI coding agents in development workflows, with the ability to evaluate and improve their output.
•An experimental mindset, with the ability to form hypotheses, analyze measurements, and communicate technical findings clearly.
•Curiosity, initiative, and enthusiasm for learning new technologies.
•GPU or AI accelerator programming.
•Embedded systems or bare-metal programming.
•Performance profiling, benchmarking, or optimization.
•LLM inference fundamentals and architectures.
What they offer
•Work on cutting-edge AI technology: contribute to the development of next-generation AI inference hardware and software.
•Gain hands-on experience: apply university knowledge to real silicon and challenging engineering problems.
•Own meaningful projects: build tools and solutions that directly support engineering teams.
•Learn from experienced engineers: work alongside specialists in hardware architecture, compilers, runtimes, and performance optimization.
•Explore AI-assisted development: gain practical experience building with AI coding agents and shaping how they interact with advanced hardware systems.
•Make a lasting impact: help develop performance tools and workflows that will support both current and future generations of Tensordyne technology.
