Job Description
Join NeuraVision Labs at the forefront of quantum-AI convergence to develop next-generation computational frameworks. We're seeking visionary engineers to pioneer hybrid quantum-classical systems that will redefine machine learning capabilities by 2026. Our Austin-based innovation hub offers unparalleled resources to transform theoretical quantum advantage into practical industrial applications.
As a key member of our Quantum Machine Learning division, you'll collaborate with Nobel Prize-winning physicists and top-tier AI researchers to deploy fault-tolerant algorithms across cryptography, drug discovery, and optimization challenges. We provide competitive equity packages, flexible R&D budgets, and access to IBM Quantum and D-Wave systems.
Responsibilities
- Design and implement quantum neural network architectures on hybrid computing platforms
- Develop error mitigation protocols for NISQ-era quantum machine learning models
- Create optimization algorithms leveraging quantum annealing for large-scale ML training
- Build quantum-classical pipelines for real-time data processing and inference
- Lead cross-functional projects integrating quantum acceleration with transformer networks
- Publish research in Nature Quantum Information and top-tier ML conferences
- Secure patents for novel quantum-enhanced learning methodologies
Qualifications
- PhD in Quantum Computing, Physics, or Machine Learning (MS + 3 years experience acceptable)
- Proficiency in Qiskit, Cirq, or PennyLane quantum programming frameworks
- Expertise in tensor network simulations and quantum circuit optimization
- Publication record in quantum information or advanced ML (arXiv/peer-reviewed)
- Strong Python/C++ skills with experience in PyTorch/TensorFlow ecosystems
- Familiarity with quantum error correction and fault-tolerant computing principles
- Demonstrated experience with high-performance computing and GPU acceleration
- Passion for solving NP-hard problems through quantum advantage