NormalComputing · Mountain View
Skills: Python, Accessibility
NORMAL COMPUTING | BUILD WITH US Normal is an applied AI company solving the hardest problems in AI and silicon. We build foundational hardware and software for the semiconductor industry, critical AI infrastructure, and the broader systems that power our world, in partnership with the world's most advanced institutions. We work as one team across New York City, Silicon Valley (Mountain View), London, Copenhagen, and Seoul. THE ROLE Normal's hardware runs the most memory-bound parts of large-model inference inside memory itself, rather than shuttling weights to a processor. The software that makes this programmable doesn't exist yet in any standard form. You will develop the computational methods that make LLM inference run efficiently on it. The core challenge is rethinking how operations like attention, memory access, and long-context decoding should behave on hardware that computes inside memory, rather than adapting standard GPU kernels to a new chip. Your job is to develop the algorithms that exploit this natively: understand which parts of LLM workloads are best suited to in-memory execution, design numerical methods that map onto the hardware's physical behavior, and validate them against our simulators and FPGA prototypes first, and against silicon as it arrives. This is a co-design role. The hardware and the algorithms are developed in parallel, which means you will influence architectural decisions rather than implement against a fixed specification. The strongest candidates have a deep understanding of both large model inference and the mathematics of stochastic systems, and have built systems that run on real hardware. WHAT YOU WILL OWN - Algorithm Development: Develop algorithms for LLM inference workloads running on our in-memory hardware. Success means algorithms that measurably improve LLM inference on our hardware, demonstrated in simulation and then on silicon. - Software/Hardware Co-Design: Work directly with hardware and architecture teams to sha…
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