--- title: 'Systems Performance Modeling Engineer at Tensordyne' canonical: 'https://feeny.ai/job/systems-performance-modeling-engineer-tensordyne-sunnyvale-ej22cbsn8prt' type: 'job' last_seen: '2026-09-26' --- # Systems Performance Modeling Engineer at Tensordyne - **Company:** Tensordyne - **Location:** Sunnyvale, CA - **Posted:** 2026-09-23 - **Last confirmed live:** 2026-09-26 - **Apply:** https://job-boards.greenhouse.io/tensordyne/jobs/8841923002 ## Job description About Tensordyne: Artificial intelligence (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 We are looking for a Systems Performance Modeling Engineer to build the models and tools that predict how generative AI inference workloads perform on Tensordyne systems, from a single accelerator up through rack, pod, and cluster scale. This is a hands-on engineering role for someone who likes writing simulator code, running experiments, and digging into why a prediction and a measurement don't match. Working closely with our architects and the silicon, hardware, networking, and software teams, you'll capture workload behavior, extend simulation and analytical models of our silicon, interconnect, and multi-hop fabrics, and validate them against real hardware. You'll be comfortable moving across the stack, from the model graph through collectives to the network fabric, to track down where performance is going. ## What You'll Do - Implement and extend simulation-based performance models for multimodal generative AI inference at rack, pod, and cluster scale, covering compute, memory, collective communication, and network fabric. - Model how serving strategies (tensor, pipeline, and expert parallelism, prefill/decode disaggregation, batching, and KV-cache placement) interact with Tensordyne silicon and fabric topology, and measure the effect on latency, throughput, and cost per token. - Build trace-capture and replay tooling that records real execution from our inference runtime and replays it under hypothetical silicon, system, and network configurations. - Model collective communication on multi-hop scale-out fabrics, including implementing custom collective algorithms designed for our topology. - Run calibration experiments on Tensordyne hardware as systems come up, compare them against model predictions, and fix the sources of error. - Run design-space sweeps and write up clear analyses that architects and engineering teams use in ASIC, fabric, and system configuration decisions. - Produce performance projections that support product and customer discussions. - Keep the modeling codebase fast, tested, and reproducible so other engineers can run it themselves. ## What We're Looking For - Hands-on experience building performance models, simulators, or analytical tools for ML workloads, distributed systems, or computer architecture. - Solid understanding of distributed ML execution, including parallelism strategies, collective communication (All-Reduce, All-Gather, All-to-All, etc.), and how they scale. - Working knowledge of system architecture across compute, memory, interconnect, and networking, and the ability to reason about bottlenecks between them. - Experience comparing model predictions against real measurements, and debugging where they diverge. - Strong programming skills in C++ and Python, with clean, testable, maintainable code. - Ability to take a loosely defined performance question, break it into experiments, and deliver results with minimal hand-holding. - Clear written and verbal communication, especially when presenting data and trade-offs to other engineers. - MS or higher in Computer Science, Computer Engineering, Electrical Engineering, or a related field. ## Nice to Have - Familiarity with LLM inference serving: batching, KV-cache management, disaggregated prefill/decode, and latency/throughput trade-offs. - Experience modeling or benchmarking collective communication libraries (NCCL, RCCL, or similar) on real clusters. - Background in data center or HPC networking: topologies, RDMA/RoCE, and congestion behavior. - Experience profiling ML workloads on accelerators (GPUs, TPUs, or custom ASICs). - Exposure to hardware/software co-design or early-stage architecture evaluation. - Publications or open-source contributions in ML systems, architecture, or networking. Tensordyne's culture was built on the following values - Put people first. We only succeed when our people succeed. - Ethics and integrity always; Being open, honest, and respectful of everyone. - Think Big. Be ambitious and have audacious goals of global scale. - 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 others’ differences, and embrace that there will be differences. Tensordyne is an equal opportunity employer. We believe that a diverse team is better at tackling complex problems and coming up with innovative solutions. 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 don’t reach out to Tensordyne employees or leaders about our roles -- we’ve got it covered. We don’t accept unsolicited agency resumes and we are not responsible for any fees related to unsolicited resumes. Thank you for your understanding. ## 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 Technical Recruiter](https://feeny.ai/job/senior-technical-recruiter-tensordyne-sunnyvale-tdxe8gm36swb) — Sunnyvale, CA - [Mid Level ASIC Design Engineer](https://feeny.ai/job/mid-level-asic-design-engineer-tensordyne-sunnyvale-9cpjjf3thgkc) — Sunnyvale, CA - [System Software Engineer, Networking](https://feeny.ai/job/system-software-engineer-networking-tensordyne-sunnyvale-vktf2pjtpv11) — Sunnyvale, CA - [Sr. ASIC Verification Engineer](https://feeny.ai/job/sr-asic-verification-engineer-tensordyne-sunnyvale-andd97brbbst) — Sunnyvale, CA - [System Software/Embedded Engineer (Diagnostics)](https://feeny.ai/job/system-software-embedded-engineer-diagnostics-tensordyne-sunnyvale-hxm00tph943z) — Sunnyvale, CA - [Sr. Staff ASIC Verification Engineer](https://feeny.ai/job/sr-staff-asic-verification-engineer-tensordyne-sunnyvale-nwmar2c6228e) — Sunnyvale, CA - [Staff Hardware Design Engineer](https://feeny.ai/job/staff-hardware-design-engineer-tensordyne-sunnyvale-mqcw91egw5j0) — Sunnyvale, CA - [Hardware Design Engineer](https://feeny.ai/job/hardware-design-engineer-tensordyne-sunnyvale-xg5sx8cqgfct) — Sunnyvale, CA - [Sr. Hardware Sustaining-DVT Engineer](https://feeny.ai/job/sr-hardware-sustaining-dvt-engineer-tensordyne-sunnyvale-3r3nr1q256h2) — Sunnyvale, CA - [Principal ASIC Architect](https://feeny.ai/job/principal-asic-architect-tensordyne-sunnyvale-6a0sy92vgx3m) — Sunnyvale, CA