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About Normal Computing 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…
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Develop computational methods and algorithms for transformer and diffusion workloads to run efficiently on stochastic analog processing-with-memory hardware. Collaborate with hardware and architecture teams to co-design the chip's native computing capabilities and validate them via simulation or real silicon.
Requires deep expertise in large model inference, stochastic systems, and implementing algorithms close to hardware using Python and a systems language. A PhD in a quantitative field and experience with non-von-Neumann architectures are highly preferred.
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.
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: understand what transformer and diffusion workloads are well-suited to stochastic analog execution, design numerical methods that map onto the hardware's physical dynamics, and validate 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 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, not just in theory.
Algorithm Development: Develop algorithms for transformer inference workloads running on stochastic analog processing-with-memory hardware.
Software/Hardware Co-Design: Work directly with hardware and architecture teams to shape what the chip can and should compute natively.
Numerical Methods: Design numerical methods that exploit thermal noise and analog dynamics rather than working around them.
Evaluation & Benchmarks: Build evaluation frameworks and benchmarks that characterize algorithm behavior on real hardware or simulation.
Workload Translation: Translate insights about model workloads into constraints and opportunities for hardware design.
Rapid Prototyping: Prototype and iterate rapidly as hardware evolves from simulation to silicon.
Deep understanding of large model inference: attention mechanisms, KV cache, long-context decoding, memory bandwidth constraints
Experience with inference optimization: quantization, sparsity, kernel fusion, or memory-efficient attention
Familiarity with stochastic systems, probabilistic methods, numerical analysis, or analog computation
Experience implementing algorithms close to hardware, not just in high-level frameworks
Comfort reasoning from first principles about what a novel substrate can do efficiently
Track record of taking ideas from theory to working implementation on real hardware
Strong programming skills in Python and at least one systems language
Collaborative instinct and ability to work across hardware, architecture, and software teams
PhD in machine learning, applied mathematics, physics, electrical engineering, or a related field
Exposure to analog or mixed-signal systems, in-memory compute, or non-von-Neumann architectures
Experience working on hardware that did not yet exist when you joined
Publications or open-source work in efficient inference, stochastic algorithms, or novel computing
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 [email protected].
Privacy Notice
By submitting your application, you agree that Normal Computing may collect, use, and store your personal information for employment-related purposes in accordance with our Privacy Policy.
AI for our most pressing crises in silicon.
At Normal, we're rewriting AI foundations to advance the frontier of reasoning and reliability in the physical world. We are tackling problems across semiconductors and industrials with a mix of interdisciplinary approaches across the full stack: from probabilistic software infrastructure and algorithms to hardware and physics, enabling AI that can reason and understand its own limits. We understand that our technology is only as powerful as the people behind it. Every employee drives significant impact within our products, often working directly with customers and embedding across our tightly-knit team. Our team members are driven by curiosity and passion for solving some of the most challenging problems in the world of atoms. Normal was founded in 2022 by engineers and scientists that pioneered industry-leading Physics + ML tools for next-gen AI at Google Brain and Google X.
Offices: New York, NY, US · San Francisco, California, US · London, England, GB · Copenhagen, DK
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