Job Description
Join Nexus Quantum Solutions at the frontier of technological evolution as we prepare for the quantum revolution of 2026. We're seeking visionary Quantum AI Research Scientists to architect the next generation of hybrid quantum-neural systems that will redefine computational boundaries. This is your opportunity to shape the future of artificial intelligence while working with world-class quantum hardware and cutting-edge machine learning frameworks.
As a key member of our Quantum AI division, you'll develop groundbreaking algorithms that leverage quantum entanglement for exponential speedups in neural network training, optimization problems, and generative AI models. Our state-of-the-art lab in San Francisco provides unparalleled resources for prototyping and testing quantum-AI hybrids with industry partners in finance, pharmaceuticals, and autonomous systems.
We offer a competitive compensation package, flexible work arrangements, and access to exclusive quantum computing resources. If you're passionate about pushing the limits of what's computationally possible and contributing to technologies that will define 2026 and beyond, we invite you to apply.
Responsibilities
- Design and implement novel quantum machine learning algorithms for neural network acceleration
- Develop hybrid quantum-classical optimization frameworks for real-world applications
- Lead research in quantum error correction techniques for fault-tolerant AI systems
- Collaborate with hardware teams to prototype quantum-AI solutions on superconducting and photonic processors
- Author breakthrough research papers for Nature Quantum and IEEE journals
- Advise Fortune 500 clients on quantum AI implementation strategies for 2026 adoption
- Mentor interdisciplinary teams of physicists, ML engineers, and quantum software developers
Qualifications
- PhD in Quantum Physics, Computer Science, or related field with 3+ years research experience
- Expertise in quantum computing frameworks (Qiskit, Cirq, PennyLane) and quantum circuit design
- Proven track record publishing in quantum machine learning or quantum information theory
- Advanced proficiency in Python, TensorFlow/PyTorch, and high-performance computing
- Deep understanding of quantum error correction and fault-tolerant architectures
- Experience with variational quantum algorithms (VQE, QAOA) and quantum neural networks
- Strong background in computational complexity and quantum algorithm analysis
- Ability to communicate complex quantum concepts to technical and non-technical stakeholders