Job Description
Join Nexus Future Labs at the forefront of technological evolution. We're seeking a visionary Quantum AI Systems Architect to pioneer next-generation computational paradigms. This role bridges quantum computing, machine learning, and emergent AI architectures to redefine what's possible in 2026 and beyond. You'll design hybrid quantum-classical frameworks that solve previously intractable problems while mentoring a multidisciplinary team of researchers and engineers.
Our Austin headquarters offers a cutting-edge lab environment with access to D-Wave quantum processors, NVIDIA H100 clusters, and a collaborative culture where innovation thrives. We provide competitive equity packages, flexible work arrangements, and opportunities to publish breakthrough research in partnership with MIT and Stanford.
Responsibilities
- Architect hybrid quantum-classical AI systems for enterprise-scale optimization problems
- Lead R&D initiatives in quantum machine learning algorithms and error correction protocols
- Collaborate with hardware teams to co-design quantum processors compatible with AI workloads
- Develop patent-pending methodologies for quantum neural networks and generative models
- Mentor cross-functional teams in quantum programming (Qiskit, Cirq) and advanced AI frameworks
- Translate theoretical quantum computing concepts into deployable industrial solutions
- Present research findings at premier conferences (QIP, NeurIPS) and peer-reviewed journals
Qualifications
- PhD in Quantum Computing, Physics, Computer Science, or related field (or equivalent experience)
- 5+ years developing quantum algorithms or hybrid quantum-classical systems
- Expertise in quantum programming frameworks (Qiskit, Cirq, Q#) and high-performance computing
- Proven track record publishing in quantum machine learning or quantum error correction
- Strong Python/C++ skills with experience in AI frameworks (PyTorch, TensorFlow)
- Familiarity with quantum hardware architectures (superconducting, trapped ion, photonic)
- Experience leading technical teams and managing research roadmaps
- Deep understanding of complexity theory and computational limits of classical systems