Job Description
Join Nexus Quantum Labs at the forefront of 2026's technological revolution. We're pioneering the convergence of quantum computing and artificial intelligence to solve humanity's most complex challenges. As a Quantum AI Research Engineer, you'll architect next-generation algorithms that push the boundaries of computational possibility in our state-of-the-art Austin research facility.
We offer unparalleled resources including access to 1000+ qubit quantum processors, NVIDIA H100 clusters, and a collaborative ecosystem of Nobel laureates and Turing Award winners. Your work will directly impact breakthroughs in drug discovery, climate modeling, and cryptographic security.
What You'll Experience: Flexible hybrid work arrangements, equity grants, unlimited learning stipends, and the opportunity to publish in Nature/Science journals. Our Austin campus features quantum labs, VR collaboration spaces, and a rooftop quantum garden.
Responsibilities
- Design and implement hybrid quantum-classical machine learning models for 1000+ qubit systems
- Develop error-corrected quantum neural networks for pattern recognition in complex datasets
- Create optimized quantum algorithms for NP-hard problems in logistics and materials science
- Lead cross-functional teams of physicists, computer scientists, and domain experts
- Publish breakthrough research in top-tier journals and industry conferences
- Patent novel quantum AI methodologies with commercialization potential
- Mentor junior researchers in quantum programming paradigms
Qualifications
- PhD in Quantum Computing, AI, or related field with 3+ years research experience
- Expertise in quantum programming languages (Qiskit, Cirq, Q#) and circuit optimization
- Proficiency in Python/C++ with PyTorch/TensorFlow integration experience
- Published work in quantum machine learning or quantum information theory
- Deep understanding of quantum error correction and fault-tolerance protocols
- Experience with high-performance computing (HPC) architectures
- Strong background in linear algebra, probability, and statistical physics