Research Engineer, Algorithms

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 worlds largest semiconductor companies and the advanced ASICs they make possible. Backed by $85M from the worlds 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 Normals 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.

Normals 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 hardwares 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

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IC