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Information Technology 🏢 Full Time ⭐️ Verified

AI Futurist

QuantumLeap Dynamics
San Francisco
Estimated Salary
USD 180.000 – USD 250.000
New
Live Update
2 Juli 2026
Deadline
2 Jul 2027

Job Description

Join QuantumLeap Dynamics as we pioneer the next frontier of human-machine collaboration. We seek an AI Futurist to architect intelligent systems that will redefine industries by 2026. This visionary role combines cutting-edge AI research with strategic foresight to build tomorrow's solutions today. You'll collaborate with Nobel laureates and disrupt industries while enjoying unparalleled autonomy in our state-of-the-art San Francisco headquarters.

Responsibilities

  • Design and deploy generative AI models capable of autonomous innovation and strategic foresight
  • Lead cross-functional teams to develop predictive algorithms for 2026 market landscapes
  • Create ethical frameworks for advanced AI systems operating beyond current regulatory boundaries
  • Architect human-AI symbiotic interfaces for seamless knowledge transfer and decision augmentation
  • Develop quantum-resistant AI architectures for secure next-gen data ecosystems
  • Establish industry benchmarks for AI consciousness and emergent behavior studies
  • Partner with governments to shape 2026 AI policy and governance frameworks

Qualifications

  • PhD in AI, Cognitive Science, or Quantum Computing with 5+ years applied research
  • Published work on AGI safety, emergent AI behavior, or neural-symbolic integration
  • Proven track record in deploying AI systems with >99.99% reliability in production
  • Expertise in transformer architectures, reinforcement learning, and quantum computing
  • Experience leading teams developing autonomous AI agents with self-improvement capabilities
  • Deep understanding of 2026 technology adoption curves and market disruption vectors
  • Certification in AI ethics from IEEE or equivalent governing body

Required Skills

Artificial Intelligence Machine Learning Quantum Computing Strategic Foresight Neural Networks AI Ethics Generative Models Reinforcement Learning

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