Job Description
Join QuantumLeap Labs at the forefront of technological evolution as we pioneer the 2026 Initiative—a groundbreaking project to redefine human-machine collaboration. We're seeking visionary researchers to develop next-generation AI systems that will shape the future of autonomous decision-making, quantum computing interfaces, and ethical AI frameworks. This role offers unparalleled opportunities to publish in top-tier journals, lead patent portfolios, and collaborate with Nobel laureates in our state-of-the-art San Francisco R&D center.
Our ideal candidate thrives at the intersection of theoretical innovation and practical application, pushing boundaries in machine learning, neural architecture design, and emergent technology ethics. You'll work in an agile environment with unlimited resources, flexible schedules, and dedicated innovation time to explore uncharted territories of computational intelligence.
Responsibilities
- Design and implement novel neural network architectures for quantum-classical hybrid systems
- Lead cross-functional teams in prototyping AI-driven solutions for autonomous systems
- Develop ethical frameworks for bias mitigation in high-stakes decision algorithms
- Author whitepapers and peer-reviewed publications on emergent AI capabilities
- Collaborate with hardware teams to optimize AI models for next-gen quantum processors
- Mentor junior researchers through our Quantum Academy talent development program
Qualifications
- PhD in Computer Science, AI, or Quantum Computing with 3+ years industry experience
- Published record in top-tier ML conferences (NeurIPS, ICML, ICLR)
- Expertise in transformer architectures, reinforcement learning, and quantum machine learning
- Proficiency in PyTorch/TensorFlow and quantum programming frameworks (Qiskit, Cirq)
- Demonstrated ability to lead complex research projects from concept to production
- Deep understanding of AI ethics and responsible AI development principles
- Strong background in computational complexity theory and algorithmic optimization