Job Description
Join Nexus Labs at the forefront of technological evolution as we pioneer the next generation of quantum-powered AI systems. We're seeking visionary Quantum AI Research Scientists to develop breakthrough algorithms that will redefine computational boundaries by 2026. Our Austin-based innovation hub offers state-of-the-art labs, collaborative research environments, and unparalleled opportunities to shape the future of human-machine symbiosis.
As part of our elite research division, you'll work alongside Nobel laureates and Turing Award winners to harness quantum mechanics and deep learning architectures. This role offers unparalleled growth potential, competitive equity packages, and the chance to publish groundbreaking research in top-tier scientific journals.
Responsibilities
- Design and implement novel quantum machine learning algorithms for real-world applications
- Lead cross-functional teams in developing hybrid quantum-classical computing frameworks
- Conduct cutting-edge research in quantum neural networks and topological data analysis
- Collaborate with hardware engineers to optimize quantum circuit designs for AI workloads
- Author peer-reviewed publications and present findings at premier scientific conferences
- Secure external funding through NSF and DARPA grant proposals
- Mentor PhD candidates and postdoctoral researchers in quantum computing methodologies
Qualifications
- PhD in Quantum Computing, Computer Science, or Physics with 3+ years postdoctoral research
- Expertise in quantum algorithms (Shor's, Grover's, VQE) and error correction techniques
- Proficiency in quantum programming languages (Qiskit, Cirq, Q#) and Python frameworks
- Published research in Nature/Science or top-tier quantum computing journals
- Strong background in machine learning frameworks (PyTorch, TensorFlow) and classical optimization
- Experience with quantum hardware platforms (IBM Q, Rigetti, IonQ) and cloud quantum services
- Demonstrated ability to lead complex research projects and secure competitive funding