Job Description
Join Nexus Future Labs at the forefront of 2026's technological revolution. We're seeking a visionary Quantum AI Research Scientist to architect next-gen systems that will redefine industries. In this pivotal role, you'll pioneer breakthroughs at the intersection of quantum computing and artificial intelligence, developing scalable solutions for autonomous systems, biotech modeling, and climate prediction. Our Austin campus features state-of-the-art quantum labs and collaborative spaces where your innovations will directly impact humanity's future trajectory.
We offer competitive equity packages, unlimited R&D budgets, and flexible hybrid work arrangements. As a cornerstone of our 2026 initiative, you'll collaborate with Nobel laureates and lead projects with global impact. If you're ready to transform theoretical possibilities into tangible realities, we invite you to shape tomorrow's technological landscape with us.
Responsibilities
- Design and implement quantum machine learning algorithms for complex system optimization
- Lead cross-functional teams in prototyping quantum-AI hybrid architectures
- Develop scalable frameworks for real-time quantum data processing
- Conduct cutting-edge research in quantum error correction and fault tolerance
- Collaborate with government agencies on quantum security protocols
- Publish peer-reviewed papers and present at international conferences
- Mentor junior researchers in quantum computing methodologies
- Drive innovation in quantum neural network implementations
Qualifications
- PhD in Quantum Computing, Physics, or Computer Science (or equivalent experience)
- 5+ years in quantum algorithm development or AI research
- Expertise in quantum programming languages (Qiskit, Cirq, or Q#)
- Proficiency with machine learning frameworks (TensorFlow/PyTorch)
- Published research in quantum machine learning or related fields
- Experience with quantum hardware platforms (IBM, Rigetti, or IonQ)
- Demonstrated ability to lead complex technical projects
- Strong background in computational complexity theory