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Quantum Systems Architect (2026)

Aether Dynamics
San Francisco
Estimated Salary
USD 180.000 – USD 260.000
New
Live Update
4 Juli 2026
Deadline
4 Jul 2027

Job Description

Are you ready to architect the future of computing? Aether Dynamics is pioneering the next generation of quantum computing infrastructure for the year 2026 and beyond. We are seeking a visionary Quantum Systems Architect to lead the design and implementation of error-corrected quantum processors integrated with classical AI systems.

In this role, you will bridge the gap between theoretical physics and practical engineering, ensuring our systems are scalable, stable, and ready for commercial deployment. If you are passionate about pushing the boundaries of what is possible in technology, we want to hear from you.

Responsibilities

  • Design and architect scalable quantum circuit layouts for superconducting and trapped-ion hardware.
  • Develop and implement advanced error correction protocols to maintain qubit fidelity in real-time environments.
  • Collaborate with cross-functional teams of physicists, hardware engineers, and software developers.
  • Optimize quantum algorithms to reduce computational overhead and latency.
  • Lead code reviews and mentor junior architects in quantum best practices.
  • Stay ahead of emerging quantum computing standards and research breakthroughs.

Qualifications

  • Ph.D. in Physics, Computer Science, Electrical Engineering, or a related quantitative field.
  • Minimum of 5 years of experience in quantum computing, superconducting hardware, or advanced simulation.
  • Proficiency in quantum programming frameworks such as Qiskit, Cirq, or PyQuil.
  • Deep understanding of quantum error correction (QEC) codes like the Surface Code.
  • Experience integrating quantum systems with classical machine learning pipelines.
  • Strong problem-solving skills and ability to work in a fast-paced, experimental environment.

Required Skills

Quantum Computing Qiskit Error Correction Superconducting Hardware AI Integration Neural Networks Python Circuit Design

Ready to Take This Challenge?

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