Job Description
Join Nexus Future Labs at the forefront of 2026's technological revolution! We're seeking a visionary Quantum AI Research Scientist to pioneer breakthroughs at the intersection of quantum computing and artificial intelligence. Shape the future by developing algorithms that leverage quantum supremacy for real-world applications in cryptography, optimization, and machine learning. Collaborate with Nobel laureates and industry pioneers in our state-of-the-art San Francisco lab.
What you'll achieve:
Design and implement quantum machine learning models that outperform classical systems by orders of magnitude. Lead research initiatives in quantum neural networks and quantum-enhanced data processing. Publish groundbreaking findings in top-tier journals and conferences. Translate theoretical concepts into patentable technologies with commercial impact.
Our vision:
At Nexus Future Labs, we're not just preparing for 2026 – we're creating it. Our multidisciplinary teams work in agile pods to accelerate technological adoption, with resources including 512-qubit quantum processors and exascale computing clusters.
Responsibilities
- Architect novel quantum AI algorithms for computational acceleration
- Lead experimental validation of quantum supremacy in ML tasks
- Develop hybrid quantum-classical frameworks for enterprise applications
- Collaborate with hardware teams to optimize quantum circuit designs
- Secure $2M+ in annual research grants from government/private entities
- Mentor PhD researchers in quantum machine learning methodologies
- Translate theoretical models into production-ready quantum software
Qualifications
- PhD in Quantum Computing, AI, or related field with 3+ years industry experience
- Published research in Nature/Science or top-tier ML/quantum conferences
- Proficiency in quantum programming languages (Qiskit, Cirq, Q#)
- Expertise in tensor networks and quantum error correction protocols
- Track record of developing production quantum machine learning systems
- Experience with high-performance computing architectures
- Strong background in theoretical physics and computational complexity