This role is focused on the modeling and simulation of high-performing AI hardware, and does not require prior knowledge of quantum computers. It will interface with a variety of internal teams and external partners with whom we are planning to collaborate. We will share more detail about this role during in-person discussions.

Develop performance models and simulators for large-scale distributed ML systems and emerging AI acceleration platforms.

Model end-to-end training and inference performance across compute, memory, networking, and communication.

Analyze workload parallelism, collective communication, scheduling, and data movement to identify system-level bottlenecks.

Explore system architectures and hardware/software tradeoffs to improve performance, scalability, and efficiency.

Build simulation frameworks for rapid design-space exploration across distributed GPU and next-generation accelerator systems.

Collaborate with ML researchers, compiler engineers, and system architects to guide next-generation AI system design.

You May Be a Fit if You Have:

PhD in Computer Engineering or Masters in Computer Engineering with 2-5 years relevant work experience

A strong research track record, including publications at top-tier venues such as ISCA, MICRO, ASPLOS, MLSys, SOSP, VLDB, OSDI, or other leading systems conferences.

Strong foundations in ML systems, distributed computing, and performance modeling.

Experience with large-scale ML training or inference systems and distributed execution frameworks.

Simulation & Modeling Engineer

Responsibilities

Qualification

Required

Benefits

Company

Sygaldry Technologies

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