Job Description
Join QuantumLeap Labs at the forefront of artificial intelligence innovation. We're pioneering breakthroughs in generative AI, autonomous systems, and quantum-computing interfaces to shape the technological landscape of 2026 and beyond. As an AI Research Scientist, you'll collaborate with Nobel laureates and industry disruptors in our state-of-the-art San Francisco campus, where your work directly impacts how humanity interacts with intelligent systems.
We offer unparalleled resourcesâincluding quantum computing clusters, petabyte-scale datasets, and a $500M R&D budgetâto accelerate your research. Our culture celebrates intellectual curiosity with flexible schedules, unlimited learning stipends, and quarterly innovation retreats in global tech capitals.
Responsibilities
- Design and implement novel deep learning architectures for next-generation autonomous decision-making systems
- Lead cross-functional research initiatives integrating quantum computing with neural networks
- Publish breakthrough findings in top-tier journals (Nature, Science, NeurIPS) and present at global conferences
- Develop ethical AI frameworks ensuring alignment with human values and regulatory compliance
- Mentor junior researchers through our proprietary Quantum Mentorship Program
- Collaborate with product teams to translate research into commercial applications
- Secure $2M+ in annual research grants through our strategic partnerships with DARPA and NSF
Qualifications
- PhD in Machine Learning, Computer Science, Quantum Physics, or related field (with 3+ years post-doc experience)
- Published record in top-tier AI/ML conferences (ICML, ICLR, CVPR) or journals
- Expertise in transformer architectures, reinforcement learning, and quantum machine learning
- Proficiency with PyTorch/TensorFlow and distributed computing frameworks (Ray, Horovod)
- Demonstrated ability to secure competitive research funding (NSF, NIH, EU Horizon grants)
- Experience deploying AI models at scale in cloud environments (AWS/GCP/Azure)
- Strong background in AI ethics, bias mitigation, and responsible AI frameworks