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Quantum AI Research Scientist

Nexus Quantum Labs
Austin
Estimated Salary
USD 180.000 – USD 280.000
Live Update
12 Mei 2026
Deadline
12 Mei 2027

Job Description

Join Nexus Quantum Labs at the forefront of technological evolution as we pioneer the next wave of quantum-AI hybrid systems. Our Austin-based research center is seeking visionary Quantum AI Research Scientists to develop revolutionary algorithms that will redefine computational boundaries in 2026 and beyond. You'll collaborate with Nobel laureates and industry pioneers in an environment where curiosity fuels breakthroughs.

We offer competitive equity packages, unlimited R&D budgets, and access to quantum hardware prototypes not yet available to the public. Your work will directly impact fields ranging from climate modeling to personalized medicine through our proprietary Quantum Neural Networks.

Responsibilities

  • Design and implement quantum algorithms leveraging AI/ML frameworks for complex optimization problems
  • Lead cross-functional teams in developing hybrid quantum-classical computing architectures
  • Author peer-reviewed research papers for Nature Quantum and IEEE journals
  • Collaborate with hardware engineers to prototype next-generation quantum processors
  • Mentor junior researchers in quantum machine learning methodologies
  • Secure multi-million dollar research grants from NSF and DARPA initiatives

Qualifications

  • PhD in Quantum Computing, Physics, or Computer Science with 5+ years industry experience
  • Published research in top-tier quantum computing or AI journals (arXiv:quant-ph, NeurIPS)
  • Proficiency in quantum programming languages (Qiskit, Cirq) and ML frameworks (PyTorch)
  • Expertise in quantum error correction and fault-tolerant systems
  • Track record of securing federal research funding
  • Experience with superconducting qubits or topological quantum computing
  • Strong background in computational complexity theory

Required Skills

Quantum Computing Machine Learning Python C++ Quantum Algorithms Quantum Error Correction Qiskit PyTorch Fault-Tolerant Systems

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