Jobs in Copenhagen, Denmark

About the role

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.

What you'll bring

• 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

Locations