Job Description
Join Nexus Labs at the forefront of technological evolution as we pioneer quantum computing solutions for 2026 and beyond. We're seeking visionary Quantum Computing Research Scientists to decode the universe's most complex challenges. In this role, you'll collaborate with Nobel laureates and industry disruptors to develop next-gen quantum algorithms that will redefine AI, cryptography, and material science. Our Austin headquarters features state-of-the-art labs, unlimited research budgets, and a culture where curiosity fuels innovation.
As part of our elite Future Technologies division, you'll access cutting-edge quantum processors and contribute to projects with global impact. We offer competitive equity packages, flexible remote options, and continuous learning stipends. Shape the future of computation while enjoying Austin's vibrant tech ecosystem.
Responsibilities
- Design and implement quantum algorithms for machine learning optimization and cryptography applications
- Lead experimental research on quantum error correction and qubit stabilization techniques
- Develop hybrid quantum-classical computing frameworks for industrial-scale problems
- Collaborate with hardware teams to prototype quantum processors operating at >99.9% fidelity
- Publish breakthrough research in Nature/Science journals and present at IEEE Quantum Week
- Mentor PhD candidates in quantum information theory through our academic partnerships
- Secure $2M+ in DARPA/NSF grants for quantum computing initiatives
Qualifications
- PhD in Quantum Physics, Computer Science, or related field (or equivalent industry experience)
- Published research in quantum algorithms or quantum information theory (arXiv/peer-reviewed)
- Proficiency in quantum programming languages (Q#, Quil, or Qiskit)
- Experience with quantum hardware platforms (IBM Quantum, Rigetti, or IonQ)
- Strong background in linear algebra and complex analysis
- Proven track record of translating theoretical models into experimental prototypes
- Expertise in machine learning frameworks (PyTorch/TensorFlow) for quantum-classical integration