Job Description
Join QuantumLeap Dynamics at the forefront of technological evolution as our 2026 AI Research Lead. We're pioneering the next wave of artificial intelligence that will redefine industries by 2026. This role offers unparalleled opportunity to shape the future of machine learning, neural networks, and autonomous systems in a collaborative, fast-paced environment.
As a key architect of our 2026 roadmap, you'll lead groundbreaking research initiatives that push the boundaries of what's possible. Our state-of-the-art lab in San Francisco provides cutting-edge resources and an interdisciplinary team of world-class innovators. We offer competitive compensation, comprehensive benefits, and the autonomy to transform visionary concepts into reality.
Responsibilities
- Architect and execute AI research strategy aligned with 2026 technological milestones
- Lead cross-functional teams in developing next-generation neural networks and ML frameworks
- Pioneer breakthrough applications in autonomous systems, predictive analytics, and generative AI
- Collaborate with product teams to translate research into scalable commercial solutions
- Drive innovation in ethical AI development and responsible deployment frameworks
- Secure federal and private funding for cutting-edge research initiatives
- Mentor PhD-level researchers and publish findings in top-tier scientific journals
Qualifications
- PhD in Computer Science, AI, or related field with 5+ years industry research experience
- Proven track record of publishing breakthrough AI research in NeurIPS/ICML/ICLR
- Expertise in transformer architectures, reinforcement learning, and quantum computing integration
- Experience leading $5M+ research budgets and managing 10+ person research teams
- Demonstrated ability to translate theoretical models into production-grade systems
- Strong background in AI ethics, bias mitigation, and regulatory compliance
- Patents or peer-reviewed publications in top-tier AI/ML journals
- Proficiency in Python, PyTorch, TensorFlow, and distributed computing frameworks