Senior Software Engineer — ML Inference & Numerics

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! The role Help make the latest AI models run well on our silicon. You'll work from model code to measured hardware performance: bring up new architectures, track down accuracy failures, and improve latency, throughput, and memory use. The bigger challenge is making that work repeatable. You'll build an agentic harness that can explore implementations, run experiments, check results, and guide the next change. Every investigation should leave behind better software, better tests, and a shorter path for the next model. We're looking for an excellent software engineer who understands LLM inference deeply and knows how to put coding agents to work. You'll own substantial parts of our SDK and work closely with engineers across models, compilers, runtime, and hardware. Your particular focus will be numerical optimization: finding where precision matters, where computation can be cheaper, and how to preserve model quality. What you'll do Build the agentic workflows. Give agents the tools to inspect models, change implementations and numerical settings, run emulation and hardware experiments, and diagnose failures. Build the feedback and evaluation that make the results trustworthy. Bring up new models quickly; make them go brrrr. Work with engineers across the stack to take unfamiliar architectures from reference code to validated, performant execution on our accelerator. Turn what you learn into reusable model support and SDK improvements. Optimize numerics. Explore precision, accumulation, and quantization choices across the model. Understand how they interact with kernels, KV caches, memory, and distributed execution, then measure the gains in latency, throughput, and memory use against model quality on silicon. Make numerical quality measurable. Build conversion and emulation software that lets you test numerical choices quickly. Compare activations and outputs against reference models, trace accuracy regressions to their source, and validate changes with end-to-end model evaluations. Ship software that lasts. Own design, implementation, testing, and delivery. Build clear APIs that are easy to use for agents. Make difficult bugs reproducible and keep them from returning. Know when to build fast, and when to build thoroughly. Shape the next chip. Work with hardware architects to test how numerical formats and arithmetic choices affect real models. Turn those findings into better hardware and algorithm designs. What we need from you You understand LLM inference end to end. You can read a new model's implementation and reason about prefill, decode, attention, KV ca