Research Engineer, Algorithms

Normal Computing Corporation • Palo Alto, CA

14h

About The Position

Normal Computing is an applied AI company developing foundational hardware and software for the semiconductor industry, AI infrastructure, and other systems. The Research Engineer, Algorithms will develop computational methods that enable efficient large language model (LLM) inference on Normal’s in-memory hardware, which performs memory-bound inference workloads inside memory rather than moving weights to a processor.

This is a hardware-software co-design role. The engineer will rethink how operations such as attention, memory access, and long-context decoding should work on in-memory computing hardware, identify which parts of LLM workloads best fit that hardware, and develop numerical methods suited to its physical behavior. Algorithms will be evaluated first with simulators and FPGA prototypes and later on silicon as it becomes available. The role works closely with hardware and architecture teams, contributing to architectural decisions while the hardware and algorithms are developed in parallel.

Requirements

• Deep understanding of large-model inference, including attention mechanisms, KV cache, long-context decoding, and memory-bandwidth constraints.
• Experience optimizing inference through methods such as 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.
• Ability to reason from first principles about what a novel computing substrate can do efficiently.
• Track record of taking ideas from theory to working implementations on real hardware.
• Strong programming skills in Python and at least one systems language.
• Ability to collaborate across hardware, architecture, and software teams.

Nice To Haves

• 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 joining the organization.
• Publications or open-source work in efficient inference, stochastic algorithms, or novel computing.

Responsibilities

• Develop algorithms for LLM inference workloads running on in-memory hardware and demonstrate measurable inference improvements in simulation and on silicon.
• Work with hardware and architecture teams to shape which computations the chip should support natively.
• Design numerical methods that exploit thermal noise and analog dynamics.
• Build evaluation frameworks and benchmarks to characterize algorithm behavior on real hardware or in simulation.
• Translate insights about model workloads into constraints and opportunities for hardware design.
• Prototype and iterate rapidly as hardware develops from simulation to silicon.
• Optimize performance at both the gate and algorithmic levels.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed