Senior Software Engineer, Inference Performance

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 You'll build the software stack that takes a model from code down to the hardware: the abstractions, kernels, libraries, and tools that engineers and agents use to reach peak performance. Help optimize the latest AI models on our silicon. Our hardware is designed to be straightforward to program, so the goal is not to hide it. The goal is to expose the right level of control at each layer, from our compiler-driven Python SDK to our low-level Rust programming stack, and to make every performance gap measurable and every fix permanent. We're looking for a builder who understands hardware deeply and develops with agents by default. You'll own substantial parts of one of these layers and work closely with engineers across models, compilers, runtime, and hardware. Your focus is performance: where cycles, bandwidth, and communication go, and how to win them back. Number formats and model accuracy are owned by our conversion and emulation team; you'll partner with them where precision and speed meet. What you'll do Build the stack. Design APIs, kernel abstractions, and libraries in our Python SDK or Rust stack so model engineers and agents can reach high performance at the layer that fits their problem. Make models fast. Take priority workloads, from LLMs to multimodal models, to peak performance on real silicon. Profile, find the bottleneck across kernels, compiler, runtime, and parallelism, and fix it at the right layer. Close the gap between model and measurement. Compare measured performance against analytical models and simulation. Explain every material gap, and turn the explanation into a fix or a better model. Build the agentic performance loop. Give agents the profilers, traces, counters, and benchmark harnesses to form a hypothesis, change code, and measure the result in minutes. Make the results trustworthy enough to act on. Set the bar for performance. Define how we measure, track regressions, and report results. Build for the next chip. Design abstractions that carry forward to future hardware generations. Work with hardware architects to test how architectural choices affect real workloads. What we need from you You understand hardware performance. You've optimized AI workloads on GPUs or custom accelerators and can reason about compute utilization, memory hierarchy, latency hiding, synchronization, and multi-chip scaling. You build excellent software. You've owned substantial systems from design through production. You're strong in Python and/or at least one systems language such as Rust, C++, or C, and rigorous about