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Quantum Computing Research Lead

Nexus Future Labs
Austin
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
16 Mei 2026
Deadline
16 Mei 2027

Job Description

Join Nexus Future Labs at the forefront of technological evolution as we pioneer quantum computing solutions for 2026 and beyond. As a Quantum Computing Research Lead, you'll architect next-gen quantum algorithms and lead cross-disciplinary teams to solve humanity's most complex challenges. We offer competitive equity packages, flexible hybrid work arrangements, and access to state-of-the-art quantum hardware.

Our Austin-based R&D hub collaborates with MIT, NASA, and global Fortune 500 companies to develop quantum-resistant cryptography, quantum machine learning frameworks, and quantum-optimized logistics systems. This role directly shapes the quantum revolution that will redefine computing by 2026.

Responsibilities

  • Design and implement quantum algorithms for real-world applications in cryptography and optimization
  • Lead a team of 5-7 quantum researchers and software engineers
  • Develop quantum error correction protocols for fault-tolerant systems
  • Collaborate with hardware teams to bridge quantum software-hardware integration
  • Publish peer-reviewed research in top quantum computing journals
  • Secure $2M+ annual research funding through government and industry grants
  • Mentor PhD candidates and postdoctoral researchers

Qualifications

  • PhD in Quantum Computing, Physics, or Computer Science (or equivalent experience)
  • 5+ years in quantum algorithm development or quantum hardware research
  • Published research in Nature/Science or top-tier quantum computing conferences
  • Expertise in quantum programming languages (Qiskit, Cirq, Quipper)
  • Strong background in linear algebra, quantum mechanics, and complexity theory
  • Experience leading technical teams and managing research budgets
  • Track record of translating quantum theory into practical applications

Required Skills

Quantum Computing Qiskit Cirq Cryptography Machine Learning Algorithm Design Research Leadership Fault-Tolerant Systems

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