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Normal Computing is an applied AI company building foundational hardware and software for the semiconductor industry and critical AI infrastructure. The company develops specialized hardware that performs memory-bound computations inside memory itself, rather than shuttling weights to a processor.
As a Research Engineer, Algorithms, you will develop the computational methods and algorithms that make large language model (LLM) inference run efficiently on this novel in-memory hardware. The core technical 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.
You will own algorithm development for LLM inference workloads, working directly with hardware and architecture teams to shape what the chip can and should compute natively. This is a co-design role where hardware and algorithms are developed in parallel, meaning you will influence architectural decisions rather than implement against a fixed specification. Key responsibilities include:
- Develop algorithms that exploit in-memory execution natively; understand which parts of LLM workloads are best suited to this approach
- Design numerical methods that map onto the hardware's physical behavior and validate them against simulators and FPGA prototypes before silicon arrives
- Work directly with hardware and architecture teams to shape chip capabilities
- Design numerical methods that exploit thermal noise and analog dynamics
- Build evaluation frameworks and benchmarks that characterize algorithm behavior on real hardware or simulation
- Translate insights about model workloads into constraints and opportunities for hardware design
- Prototype and iterate rapidly as hardware evolves from simulation to silicon
- Optimize performance at both the gate level and the algorithmic level
The strongest candidates have deep understanding of both large model inference and the mathematics of stochastic systems, and have built systems that run on real hardware.
REQUIREMENTS:
- 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, beyond 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
BONUS:
- PhD in machine learning, applied mathematics, physics, electrical engineering, or 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
