Architecture Modeling Engineer

Arago•Paris, Île-de-France

About The Position

Arago's mission is to re-engineer the foundations of computing from first principles. The explosive growth of AI is pushing the industry to rethink how processors are built. Arago is meeting that challenge with a proprietary technology that fuses optical and CMOS technologies to deliver an order-of-magnitude increase in performance. Arago is the fastest, and currently the only, company to have built such a processor. It's backed by leading deep-tech investors and some of the most respected figures in semiconductors and computing. Our work is guided by three clear values: do great things, move with high velocity, and operate as one unit. We work in a demanding environment where constant learning, ownership, and execution are expected, and where exceptional people have the opportunity to do their life’s work. As Architecture Modeling Engineer you will own performance modeling and workload analysis for Arago's optical AI accelerator, working across hardware and software teams to evaluate design choices, shape the programming model, and optimize key compute kernels. This role turns workload requirements into architecture decisions and, ultimately, high-performance software on silicon.

Requirements

Strong mathematical background, particularly in linear algebra, probability, numerical methods, and quantitative performance analysis.
Strong knowledge of computer architecture, including compute pipelines, memory hierarchies, interconnects, and parallel execution.
Strong C/C++ and Python programming skills.
Experience analyzing and optimizing performance-critical kernels or low-level compute workloads.
Good understanding of AI/ML workloads, tensor operations, data movement, and common execution patterns.
Ability to work across hardware and software teams and translate workload behavior into architecture and software requirements.
Language: English at a proficient level.

Nice To Haves

Strong industry knowledge about AI hardware, GPUs, memory generations, rack-scale computing.
Experience building performance models, simulators, or analytical models for CPUs, GPUs, NPUs, or other accelerators.
Experience with profiling, benchmarking, and identifying hardware or software performance bottlenecks.
Experience in novel computing architectures: analog computing, in-memory computing, heterogeneous systems.

Responsibilities

Develop fast, flexible performance models to evaluate AI accelerator architecture choices and guide early design decisions.
Work closely with the software team to characterize key AI workloads, kernels, dataflows, and system bottlenecks.
Work closely with hardware architects to model compute, memory, interconnect, and scheduling behavior across proposed architectures.
Drive rapid performance studies and trade-off analyses across throughput, latency, utilization, bandwidth, and efficiency.
Help define and validate the programming model and workload mapping strategy for the accelerator.
Following architecture definition, work hands-on with the software team to optimize compute kernels and key workloads for the target hardware.
Build lightweight simulation, profiling, and analysis tools that support fast iteration from architecture exploration through software optimization.