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Normal Computing is developing novel silicon chips that leverage thermal noise for computation, offering significant improvements in AI inference efficiency. We are seeking a Research Engineer to design algorithms for running AI inference on our unique thermodynamic hardware. This co-design role involves rethinking computational methods for stochastic analog processing, influencing architectural decisions, and evaluating performance on real silicon. The ideal candidate has a deep understanding of large model inference, stochastic systems, and a proven track record of implementing algorithms on hardware.
Normal Computing builds silicon that turns thermal noise from an obstacle into a computational resource. Conventional chips spend most of their energy forcing determinism onto physics; ours compute with it. Stochastic, in-memory, asynchronous: the result is 10-100× more AI inference per dollar, per watt.
We co-design the full stack: AI-native EDA systems in production with the world's largest semiconductor companies, and the advanced ASICs they make possible. Backed by $85M+ from the world's leading deep-tech investors and built by scientists, engineers, and operators from the labs that built modern computing.
Normal works as one team across New York, Silicon Valley, London, Copenhagen, and Seoul. We hire people who want the hardest version of their craft, across every discipline, at every seniority.
As a Research Engineer focused on inference, you will develop the computational methods that make AI inference run efficiently on Normal's thermodynamic hardware. The core challenge is not adapting standard GPU kernels to a new chip. It is rethinking how operations like attention, memory access, and long-context decoding behave when the underlying substrate uses stochastic analog computation in memory rather than conventional digital logic.
Normal's ASICs run the heaviest operations of large model inference inside memory itself. Your job is to develop the algorithms that exploit this natively: understanding what transformer workloads are well-suited to stochastic analog execution, designing numerical methods that map onto the hardware's physical dynamics, and validating them against real silicon or high-fidelity simulation.
This is a co-design role. The hardware and the algorithms are developed in parallel, which means you will influence architectural decisions, not just implement against a fixed spec. The strongest candidates have a deep understanding of both large model inference and the mathematics of stochastic systems, and have built things that run on real hardware, not just in theory.
Equal Employment Opportunity Statement
Normal Computing is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, veteran status, or any other legally protected status.
Accessibility Accommodations
Normal Computing is committed to providing reasonable accommodations to individuals with disabilities. If you need assistance or an accommodation due to a disability, please let us know at accommodations@normalcomputing.com.
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