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Sygaldry Technologies

Provides quantum-accelerated AI server infrastructure

AI Systems Performance Architect

Hardware Systems IntegrationConfirmed live in the last 24 hours
Full-timeIn-person

About the Job

Requirements

  • A PhD in Computer Engineering or Electrical Engineering and 2–5 years of 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 machine-learning systems, distributed computing, and performance modeling.
  • Experience with large-scale machine-learning training or inference systems and distributed execution frameworks.
  • Understanding of model parallelism, data parallelism, collective communication, and distributed scheduling.
  • Experience building simulators, analytical performance models, or system-level benchmarking tools.
  • Familiarity with GPU clusters, high-performance networking, and AI accelerator systems.
  • Ability to reason from first principles, identify bottlenecks, and independently tackle open-ended research problems.

Responsibilities

  • Develop performance models and simulators for large-scale distributed machine-learning 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 machine-learning researchers, compiler engineers, and system architects to guide next-generation AI system design.

About the Company

Sygaldry Technologies

Provides quantum-accelerated AI server infrastructure

Sygaldry Technologies builds quantum-accelerated AI servers that bring quantum capabilities into AI models and workflows. It achieves this by integrating multiple qubit modalities within a fault-tolerant architecture to form utility-scale quantum computers that accelerate training and inference. Unlike others that rely on a single quantum technology, Sygaldry combines diverse qubit approaches in a single platform to tailor performance. Its goal is to provide affordable, sustainable, personalized, and secure superintelligence through practical quantum AI infrastructure.

Company size
11-50
Company stage
Series A
Total funding
$139.1M
Headquarters
Ann Arbor, MI
Founded
2024

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