Job Description
Join QuantumLeap Labs at the forefront of 2026's technological frontier as we pioneer breakthroughs in artificial general intelligence. We're seeking a visionary AI Research Scientist to architect next-generation neural architectures that will redefine human-machine collaboration. Our multidisciplinary team operates at the intersection of quantum computing, neuromorphic engineering, and ethical AI development. You'll leverage our cutting-edge 7nm quantum-accelerated infrastructure to solve previously intractable problems in autonomous systems and predictive modeling. This role offers unparalleled opportunities to shape the trajectory of human progress while working in our state-of-the-art San Francisco research facility.
Responsibilities
- Design and implement novel neural network architectures optimized for quantum-accelerated computing environments
- Lead cross-functional research initiatives in explainable AI and ethical machine learning frameworks
- Develop predictive models achieving >99.7% accuracy in complex multi-variable systems
- Publish groundbreaking research in top-tier AI/ML conferences and journals
- Mentor junior researchers through our proprietary 2026 Innovation Accelerator program
- Collaborate with neuromorphic engineering teams to create brain-inspired computing solutions
- Secure patents for proprietary AI algorithms and quantum machine learning techniques
Qualifications
- PhD in Computer Science, AI, or related field with 5+ years of industry research experience
- Published record in NeurIPS, ICML, or equivalent tier conferences (minimum 3 papers)
- Expertise in transformer architectures, reinforcement learning, and quantum machine learning
- Proficiency with PyTorch/TensorFlow and quantum programming frameworks (Qiskit, Cirq)
- Demonstrated experience scaling ML models to 10^9+ parameter systems
- Strong background in computational complexity theory and algorithm optimization
- Track record of translating research into production AI systems with measurable impact
- Certification in Responsible AI Governance (RAIG) or equivalent ethical AI training