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Information Technology 🏢 Full Time ⭐️ Verified

Senior Quantum Algorithm Developer

Nexus Quantum Labs
Austin
Estimated Salary
USD 180.000 – USD 250.000
Live Update
9 Juli 2026
Deadline
9 Jul 2027

Job Description

Join Nexus Quantum Labs at the forefront of computational revolution! We're pioneering quantum algorithms that will redefine 2026's technological landscape. As a Senior Quantum Algorithm Developer, you'll architect next-gen solutions for cryptography, optimization, and machine learning. Our Austin headquarters features cutting-edge labs and collaborative spaces where your expertise in quantum mechanics and software engineering will shape tomorrow's innovations.

What You'll Experience:
• Mentorship from quantum pioneers
• Flexible hybrid work arrangements
• Comprehensive wellness program
• Participation in industry-defining research

Responsibilities

  • Design and implement quantum algorithms for complex computational problems
  • Collaborate with physicists and software engineers to bridge theory and practice
  • Optimize quantum circuits for NISQ-era hardware limitations
  • Lead open-source contributions to quantum programming frameworks
  • Develop error mitigation strategies for practical quantum applications
  • Present research findings at international quantum computing conferences
  • Guide junior researchers through quantum algorithm development lifecycle

Qualifications

  • PhD in Physics, Computer Science, or related field (MS with 5+ years experience)
  • Expertise in quantum programming languages (Q#, Qiskit, Cirq)
  • Published research in quantum algorithms or quantum information theory
  • Proficiency in Python and high-performance computing frameworks
  • Experience with quantum hardware platforms (IBM Q, Rigetti, IonQ)
  • Strong background in linear algebra and probability theory
  • Track record of translating theoretical concepts to practical implementations

Required Skills

Quantum Computing Qiskit Cirq Q# Python Quantum Algorithms High-Performance Computing Cryptography Machine Learning Linear Algebra

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