Job Description
Join Nexus Labs at the frontier of computational innovation as we pioneer quantum-AI systems for 2026 and beyond. We're seeking a visionary Quantum AI Research Engineer to develop hybrid quantum-neural architectures that will redefine machine learning capabilities. This role offers unparalleled access to our state-of-the-art quantum computing lab and collaboration with Nobel Prize-winning researchers. You'll shape the next generation of AI while working on projects with global impact in cryptography, drug discovery, and climate modeling.
What you'll achieve: Design quantum algorithms for real-world applications, publish breakthrough research in top-tier journals, and mentor a team of emerging quantum scientists. Our competitive benefits package includes equity, unlimited PTO, and dedicated R&D funding for your experimental projects.
Responsibilities
- Develop hybrid quantum-classical machine learning models for enterprise-scale applications
- Design error-corrected quantum circuits optimized for NISQ-era hardware
- Lead cross-functional projects with AI, physics, and cybersecurity teams
- Author peer-reviewed research and present findings at international conferences
- Architect quantum-resistant encryption protocols for 2026 security standards
- Mentor junior researchers in quantum algorithm development
- Collaborate with hardware teams to optimize quantum-AI stack performance
Qualifications
- PhD in Quantum Computing, Machine Learning, or Computational Physics
- 3+ years of experience with quantum programming frameworks (Qiskit, Cirq, or PennyLane)
- Proficiency in Python, TensorFlow, and high-performance computing environments
- Published research in quantum machine learning or quantum information theory
- Deep understanding of quantum error correction and fault-tolerant architectures
- Experience with cloud quantum computing platforms (IBM Quantum, Amazon Braket)
- Strong background in linear algebra, probability theory, and statistical modeling
- Demonstrated ability to translate theoretical concepts into practical implementations