Job Description
Join FutureNexus Labs at the forefront of technological evolution as we pioneer the next wave of quantum-AI integration for 2026 and beyond. We're seeking visionary Quantum AI Research Engineers to architect groundbreaking solutions that will redefine computational paradigms. In this pivotal role, you'll collaborate with Nobel laureates and industry disruptors to develop hybrid quantum-neural systems capable of solving previously intractable challenges in medicine, climate modeling, and materials science.
Our Austin-based innovation hub offers unparalleled resources, including a 512-qubit quantum processor and dedicated AI supercomputing clusters. You'll contribute to patent-pending research while shaping ethical frameworks for next-generation autonomous systems. This isn't just a job—it's your opportunity to engineer humanity's technological future.
Responsibilities
- Design and implement quantum machine learning algorithms for real-world problem domains
- Develop hybrid quantum-classical neural network architectures for 2026-era applications
- Lead cross-functional R&D projects integrating quantum computing with AI/ML pipelines
- Author peer-reviewed publications and contribute to open-source quantum-AI frameworks
- Collaborate with hardware teams to optimize quantum circuit performance on emerging processors
- Define ethical guidelines for autonomous quantum decision-making systems
- Mentor junior researchers in quantum algorithm development
Qualifications
- PhD in Quantum Computing, Machine Learning, or Computational Physics (or equivalent experience)
- Expertise in quantum programming languages (Q#, Qiskit, Cirq) and quantum circuit optimization
- Proven track record publishing in Nature/Science or top-tier ML conferences
- Proficiency in Python, TensorFlow/PyTorch, and high-performance computing frameworks
- Experience with quantum error correction and fault-tolerant computing architectures
- Deep understanding of quantum mechanics foundations and entanglement protocols
- Demonstrated ability to translate theoretical concepts into practical implementations