--- title: 'Working Student - LLM Inference Performance at Tensordyne' canonical: 'https://feeny.ai/job/working-student-llm-inference-performance-tensordyne-munich-ep2f11b2p27s' type: 'job' last_seen: '2026-11-06' --- # Working Student - LLM Inference Performance at Tensordyne - **Company:** Tensordyne - **Location:** Munich, Germany - **Posted:** 2026-11-01 - **Last confirmed live:** 2026-11-06 - **Apply:** https://job-boards.greenhouse.io/tensordyne/jobs/8926410002 ## Job description ## 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 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.. ## Nice to Have Experience in one or more of the following areas would be a plus: - GPU or AI accelerator programming.. - Embedded systems or bare-metal programming.. - Performance profiling, benchmarking, or optimization.. - LLM inference fundamentals and architectures.. Why Join Tensordyne? - Work on cutting-edge AI technology: Contribute to the development of next-generation AI inference hardware and software.. - Gain hands-on experience: Apply your university knowledge to real silicon and challenging engineering problems.. - Own meaningful projects: Build tools and solutions that directly support our 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.. Tensordyne's culture was built on the following values - Put people first. We only succeed when our people succeed. - Ethics and integrity always. Be open, honest, and respectful of everyone. - Think Big. Be ambitious and have audacious goals. - Aim for excellence. Quality and excellence count in everything we do. - Own it and get it done. Results matter! - Make each person better together than they would be as an individual. - Embrace each other’s differences, and embrace that there will be differences. Tensordyne is an equal opportunity employer. We believe diverse teams are better equipped to solve complex problems and build exceptional technology. All qualified applicants will receive consideration for employment without regard to age, color, gender identity or expression, marital status, national origin, disability, protected veteran status, race, religion, pregnancy, sexual orientation, or any other characteristic protected by applicable laws, regulations, and ordinances. A note to recruitment agencies: Please do not contact Tensordyne employees or leaders regarding this role. We do not accept unsolicited agency resumes and are not responsible for fees associated with unsolicited submissions. ## About Tensordyne ## Company Overview - **One-liner**: Tensordyne builds full-stack AI inference systems that use proprietary logarithmic math and custom 3nm silicon to dramatically reduce power, cost, and rack space for running large-scale generative AI models. - **Entity Type**: Private (Series C funded) - **Headquarters**: Sunnyvale, California, United States (co-headquarters also in Munich, Germany) - **Founded**: 2018 (based on first funding round in 2018) - **Founders**: Gilles Backhus (Co‑Founder, VP of AI & Product), RK Anand (Co‑founder & Chief Product Officer) ## Core Business - **Primary industry**: AI hardware and infrastructure (computer hardware manufacturing, deep learning silicon, systems engineering) - **Target customers**: Hyperscalers, Neo-Cloud data centers, and large enterprises running multimodal generative AI workloads - **Mission**: “Innovating at the zeroth layer – rewrite the numbers so intelligence flows with a fraction of today’s energy and cost.” ## Products & Services - **[Tensordyne Napier Inference System](https://www.tensordyne.ai/inference-system)**: An integrated rack‑scale AI inference platform combining custom logarithmic processors (3nm), HBM/SRAM memory, and a sub‑microsecond interconnect. Delivered as a TDN72 pod (72 chips) that scales into a full rack, supporting standard token serving and disaggregated inference. Fully air‑cooled and designed to fit into existing data center infrastructure. ## Market Standing - **Valuation**: Not publicly disclosed - **Key Metrics**: - Annual Revenue: $4.5M (estimated, per LinkedIn) - Total Funding: $211.05M across 5 rounds - **Notable Investors/Partners**: GreatPoint Ventures (led Series A), Celesta Capital (led Series B), Premji Invest (led Series C), HSBC Innovation Banking (debt financing) - **Growth Signals**: Acquired Recogni GmbH; 89 employees (growing ~1% monthly); 12 active job postings (up 20% month-over-month, 140% quarter-over-quarter); talent drawn from top AI teams (Argo AI, Intel, NVIDIA, BMW, Bosch AI Center) ## Competitive Advantages - **Logarithmic compute**: Replaces energy‑intensive multiplications with additions, cutting power consumption at the root. - **Custom 3nm silicon** with built‑in interconnect logic, eliminating traditional bottlenecks. - **Sub‑microsecond interconnect** (under 1,000 ns latency, 1 TB/s any‑to‑any bandwidth) enabling near‑linear scaling across 72 chips. - **9× better space efficiency, 2× faster speed, and 10× cost savings** versus the Nvidia+Groq standard (company claim). - **Fully air‑cooled** – no complex liquid cooling required, lowering deployment friction. ## Strategic Focus Tensordyne is focused on making GenAI inference “profitable at speed and scale” by delivering a drop‑in system that dramatically reduces the total cost of ownership for hyperscale and cloud data centers. Current priorities include scaling production of the Napier system, expanding go‑to‑market with enterprise sales, and deepening the logarithmic quantization ecosystem to support any framework or model. ## Why Work Here - **Culture**: “Build cool tech” – a band of engineers, mathematicians, and chip designers who thrive on solving hard, ambiguity‑rich problems. Values include one team, mission focus, innovate with passion, think like an owner, and act with integrity. - **Remote/Hybrid**: Remote‑friendly; output over location. In‑person gatherings happen when “magic needs to happen.” - **Engineering emphasis**: Deep technical challenges across math, silicon, systems, and software – with a fast‑moving, well‑funded environment. Team spans North America and Europe (offices in Sunnyvale, San Jose, Munich). - **Hiring process**: Mutual discovery – a few calls, possibly a challenge, then fast decisions. ## Sources 1. [tensordyne.ai – About](https://www.tensordyne.ai/about) 2. [tensordyne.ai – Careers](https://www.tensordyne.ai/careers) 3. [tensordyne.ai – Inference System](https://www.tensordyne.ai/inference-system) 4. [linkedin.com – Tensordyne Company Page](https://www.linkedin.com/company/tensordyne) ## Other roles at Tensordyne - [Senior Software Engineer — ML Inference & Numerics](https://feeny.ai/job/senior-software-engineer-ml-inference-numerics-tensordyne-munich-qrmjnkcnqpkp) — Munich, Germany - [System Software / Embedded Engineer](https://feeny.ai/job/system-software-embedded-engineer-tensordyne-sunnyvale-1rwc6exvavsc) — Sunnyvale, CA - [System Software/Embedded Engineer (Munich)](https://feeny.ai/job/system-software-embedded-engineer-munich-tensordyne-munich-ch5fc1wh9yvj) — Munich, Germany - [Software Engineer – Cloud Services – Rust](https://feeny.ai/job/software-engineer-cloud-services-rust-tensordyne-munich-k2jksee9f5k3) — Munich, Germany - [Ops/Developer Platform Engineer](https://feeny.ai/job/ops-developer-platform-engineer-tensordyne-munich-whbc1e2asynv) — Munich, Germany - [Sr. Compiler Engineer](https://feeny.ai/job/sr-compiler-engineer-tensordyne-munich-wgebfvc0rdwm) — Munich, Germany - [Working Student - LLM Quantization & Inference Performance](https://feeny.ai/job/working-student-llm-quantization-inference-performance-tensordyne-munich-j7zc6yravggc) — Munich, Germany - [Staff ASIC STA Engineer](https://feeny.ai/job/staff-asic-sta-engineer-tensordyne-sunnyvale-9sppj4qyy64z) — Sunnyvale, CA - [Simulation & Modeling Engineer](https://feeny.ai/job/simulation-modeling-engineer-tensordyne-munich-4dhthzme0wv5) — Munich, Germany - [Director, AI Systems Solutions Engineering](https://feeny.ai/job/director-ai-systems-solutions-engineering-tensordyne-sunnyvale-2hqajfpnnvmb) — Sunnyvale, CA