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Quantum AI Systems Engineer

Nexus Innovations Inc.
Austin
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
13 Mei 2026
Deadline
13 Mei 2027

Job Description

Join Nexus Innovations at the forefront of technological evolution as we pioneer systems for 2026 and beyond. We seek a visionary Quantum AI Systems Engineer to architect next-generation solutions that bridge quantum computing and artificial intelligence. This role offers unparalleled opportunity to shape the future of computational intelligence in a dynamic, innovation-driven environment. You'll collaborate with world-class researchers to develop scalable quantum neural networks and hybrid AI systems that redefine industry capabilities.

Responsibilities

  • Design and implement quantum-classical hybrid AI architectures for real-world applications
  • Develop error-corrected quantum algorithms optimized for machine learning workloads
  • Create simulation frameworks for quantum neural network performance validation
  • Lead cross-functional teams in prototyping quantum-enhanced decision systems
  • Research and integrate emerging quantum hardware advancements into production pipelines
  • Optimize quantum AI models for scalability across distributed computing environments
  • Document system architectures and publish breakthrough findings in peer-reviewed journals

Qualifications

  • PhD in Quantum Computing, Computer Science, or Physics (or equivalent experience)
  • 5+ years developing production-level AI/ML systems
  • Expertise in quantum programming languages (Q#, Qiskit, Cirq) and frameworks
  • Proficiency in high-performance computing environments and parallel processing
  • Published research in quantum machine learning or quantum information theory
  • Experience with cloud quantum computing platforms (IBM Quantum, Amazon Braket)
  • Strong background in Python, TensorFlow/PyTorch, and low-level optimization

Required Skills

Quantum Computing Artificial Intelligence Machine Learning Qiskit Q# Cirq High-Performance Computing TensorFlow PyTorch Distributed Systems

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